Introduction

Large language models (LLMs) have an impressive ability to answer questions and follow instructions, but they often do so without checking implicit assumptions or resolving ambiguity. For example, if you ask “What’s a good pasta recipe?”, a generic model might immediately give a recipe – even though you haven’t said what ingredients you like or any dietary restrictions. A more critical-thinking AI would recognize the underspecified request and ask, “Do you have any dietary preferences or ingredients in mind?” Only after getting clarification would it provide a tailored recipe. This kind of behavior – identifying hidden assumptions, asking clarifying questions, and reasoning through ambiguity – is crucial for truly helpful, human-aligned AI assistants.

In this post, we’ll explore state-of-the-art research on how to train and fine-tune LLMs to handle implicit assumptions and ambiguous queries in natural language interactions. We’ll start with an accessible overview of why clarifying questions matter, then dive into how researchers are teaching models to detect ambiguity and seek clarification. Each section provides a high-level explanation for general readers, followed by deeper technical insights (with references to key papers) on training methods, datasets, and model techniques that encourage clarification-seeking behavior. Finally, we’ll discuss challenges and the road ahead for developing language models with better critical thinking and assumption-handling skills.

Why Clarifying Questions Matter (For Humans and AIs)

Most of us naturally ask follow-up questions when something isn’t clear – it’s a basic part of human conversation. If someone says, “I need help with bank accounts,” you’d likely respond “Which bank or what kind of help do you need?” instead of guessing blindly. Current AI models often skip this step. They tend to assume a particular interpretation and answer right away, which can lead to irrelevant or incorrect responses that frustrate users. This happens because users often (unintentionally) leave out details or make assumptions in their questions, and the AI doesn’t yet have the common-sense habit of double-checking what the user really means.

Why don’t today’s powerful LLMs automatically ask clarifying questions? Researchers have found a few reasons:

• Bias from training: Models like GPT-4 were trained with techniques (e.g. Reinforcement Learning from Human Feedback, RLHF) that reward responses judged in isolation. An answer that “looks complete” often beats a question asking for clarification during these preference comparisons. In other words, human labelers tended to prefer a confident answer over an incomplete response, so the model learned to answer even ambiguous queries by guessing an interpretation. This creates a bias against responses that admit uncertainty or request more detail.
• Lack of examples: There are very few dialogue examples in the training data where the assistant says, “I’m not sure what you mean, could you clarify?” As one study put it, “there are few dialogues including clarifying questions in the pre-training or finetuning datasets” of popular models. The model might know a question is ambiguous (internally it can often detect uncertainty) but still not behave accordingly because it was never taught that clarifying is the correct action.
• Overconfidence and user experience concerns: By design, many assistants avoid asking too many questions, fearing it might annoy users or make the AI seem less capable. Unfortunately, this can result in overconfident answers to underspecified questions. Studies show that even adding chain-of-thought reasoning or few-shot prompts only marginally improves ambiguity handling for off-the-shelf models – the model might produce a longer reasoning process internally, but still ends up picking an interpretation rather than asking the user.

The consequence is that current LLMs often respond to ambiguous or assumption-laden queries with a single interpretation, which may be wrong. This reduces their reliability and can erode user trust. Especially in high-stakes areas (legal, medical, etc.), failing to clarify critical details can lead to serious mistakes. It’s clear that the next generation of helpful AI assistants should be able to recognize when they’re missing important information and politely ask for clarification instead of forging ahead.

A Quick Example

To illustrate, consider the pasta recipe scenario again. A user asks: “What is a good pasta recipe?”

• A typical LLM might assume a generic context and answer with a standard recipe (let’s say, spaghetti aglio e olio). If the user is vegetarian or gluten-free, that answer isn’t appropriate – the model unknowingly made an assumption and got it wrong.
• A clarification-savvy LLM would respond with something like: “Sure! I can help with that. First, do you have any dietary preferences or ingredients you want to use?” If the user then says they’re vegetarian and love spicy food, the assistant can give a much better answer (maybe a recipe for spicy arrabbiata with veggies). The interaction might take one extra turn, but the outcome is far more useful.

Researchers call this ability to identify underspecified requests and resolve them through interaction “clarification in dialog”, and it’s a key part of making AI systems more aligned with user needs.

In the next sections, we’ll see how recent research is tackling this problem from multiple angles: detecting ambiguity and assumptions, training models to ask good questions, and using techniques to imbue models with more critical thinking so they don’t accept queries at face value.

Detecting Implicit Assumptions and Ambiguity

Before an AI can clarify something, it first needs to realize there is something to clarify. This means detecting ambiguity or questionable assumptions in the user’s input. Ambiguity can come in many forms: missing context (e.g. “When did he win the award?” with an unclear reference), vague requests (“I need a bank account” – which bank? what kind of account?), or inherently ambiguous questions with multiple possible answers (“Who is the fastest runner?” – in which category or time period?). Sometimes the user’s question even contains a false presupposition – for instance, “Where are the 2021 Winter Olympics being held?” assumes that there were Winter Olympics in 2021, which is not true (they skipped from 2018 to 2022).

For a non-expert, it’s easy to think “Why can’t the AI just notice these issues?” In fact, advanced models do have some ability to detect ambiguity; they just aren’t explicitly trained to act on it. Research has shown that if you ask a modern LLM whether a question is ambiguous, it can often recognize ambiguity in a yes/no form. However, without further guidance, the same model might still answer the ambiguous question directly in a normal conversation. Clearly, detection alone isn’t enough – but it’s the first step.

On the research front, several efforts are focused on systematically identifying when a query is ambiguous or contains invalid assumptions:

• Taxonomies of Ambiguity: A 2024 benchmark called CLAMBER defines a taxonomy of different ambiguity types and evaluates various LLMs on them. For example, it distinguishes lexical ambiguity (a word with multiple meanings), semantic underspecification (not enough context, like missing “when/where”), and even epistemic uncertainty (the model not having enough knowledge). Their findings were sobering: out-of-the-box models struggled across the board to reliably identify ambiguous queries, even when researchers tried prompting the model to “think carefully” via chain-of-thought. In some cases these prompting techniques made the model more confident without actually improving accuracy, which is concerning. The CLAMBER study highlighted that current models often don’t know when they don’t know, underscoring the need for dedicated training to handle ambiguity.
• Ambiguity Detection as a Task: Other works have treated ambiguity detection as its own supervised task. For instance, the CAMBIGNQ dataset (EMNLP 2023) includes a subtask specifically for identifying if a question is ambiguous. CAMBIGNQ provided 5,653 user questions from real Google queries that were labeled as ambiguous (with multiple possible interpretations) along with relevant evidence. A model can be trained on such data to output a binary judgment: ambiguous or not. In the CAMBIGNQ benchmark, even the best models only achieved about 61% F1 on ambiguity detection, meaning there is lots of room for improvement.
• Questionable Assumption Detection: When it comes to false or questionable assumptions in a query, researchers have created tasks to explicitly detect them. One paper titled (QA)^2: Question Answering with Questionable Assumptions (ACL 2023) defines a binary classification challenge: given a question, output whether it contains a false/unverifiable assumption. For example, “Does the question ‘where are the winter olympics held 2021’ contain any invalid assumptions?” – the answer should be Yes, it does. Models can be fine-tuned to answer such questions with yes/no. Interestingly, (QA)^2 also set up an end-to-end task where the model must answer the original question in a satisfactory way, which could involve correcting the assumption. There isn’t a single “right” strategy – the model could explicitly point out the false premise, answer a corrected version of the question, or ask the user to clarify what they meant. For evaluation, human judges checked if the model’s answer was acceptable given the question. This is challenging for models: one finding was that simply using a powerful GPT-3 model zero-shot resulted in only ~66% of answers being acceptable. However, using step-by-step prompting (i.e., instructing the model to reason about assumptions before answering) boosted performance by about 20 percentage points. This suggests that prompting or training a model to explicitly check assumptions (e.g., “Let’s think: does this question have a false premise?”) can significantly improve its behavior.

From the above, we see a pattern: if we prompt or fine-tune models to detect ambiguity/assumptions, they can do it to an extent. The next step is turning that detection into action – i.e. asking for clarification or otherwise resolving the ambiguity.

Before moving on, it’s worth noting a clever concept introduced in late 2024: “perceived ambiguity.” In a paper titled Aligning Language Models to Explicitly Handle Ambiguity, Kim et al. argue that whether a query seems ambiguous can depend on the model’s own knowledge. For instance, the question “When was the last time UGA won a national championship?” might not seem ambiguous to a layperson (they might assume it’s about football), but a model with a wide knowledge base knows UGA (University of Georgia) has won championships in multiple sports (football, baseball, etc.). To that model, the question is ambiguous – it could be referring to any sport. The researchers propose an approach called APA (Alignment with Perceived Ambiguity), where the model first self-checks how much uncertainty it has. In practice, they guide the model to disambiguate the query on its own (e.g., internally consider different interpretations or fill in specifics), and measure the “information gain” from that disambiguation. If a lot of information was added when the model tried to clarify the query internally, that’s a sign the query was ambiguous. Those cases are then used to train the model to explicitly ask a clarification question to the user. An interesting finding was that this method of leveraging the model’s own sense of confusion outperformed training on a fixed set of human-labeled ambiguous questions, especially for queries outside the training distribution. In short, APA got the model to say “Can you clarify your question?” when needed, without hurting its ability to answer clearly when the query was unambiguous.

To summarize this section: detecting ambiguity and assumptions is an active research area. We have new datasets and benchmarks to measure it, and techniques from straightforward classification to complex self-analysis. But detection is only half the battle – the really novel progress comes in teaching models what to do once ambiguity is recognized. That’s where clarification-seeking behavior comes in, which we’ll discuss next.

Training Models to Ask Clarifying Questions

Recognizing an ambiguous or underspecified query is important, but a helpful AI should also take initiative to resolve the ambiguity. The most direct way is to ask the user a clarifying question. This sounds simple: just ask! However, for a language model, asking a good clarifying question is a non-trivial skill. The question needs to be relevant, concise, and actually help disambiguate the user’s intent without annoying them.

This is where a lot of exciting research is happening. Broadly, researchers are exploring three approaches to imbue LLMs with clarification skills: supervised fine-tuning on clarification dialogues, innovative reward schemes (often via RL or self-play) that encourage asking questions, and architectural or prompting strategies that guide the model to intermix questions when needed.

Supervised Fine-Tuning on Clarification Data

One straightforward idea is: give the model examples of good clarification behavior and fine-tune it to imitate them. The challenge is that such examples weren’t common in traditional datasets, so teams have started creating their own:

• ClarifyingQA (Cambridge, 2022): A team from Cambridge University created a small but interesting dataset called ClarifyingQA specifically for multi-turn QA with clarifications. They took ambiguous questions from the AmbigQA dataset (which provides multiple interpretations and answers for each question) and had humans write a dialogue: (User’s ambiguous question → Assistant asks a clarifying question → User gives a clarification → Assistant answers the clarified query). They also included clear questions (which don’t need clarification) as direct QA pairs. By fine-tuning GPT-3 on this mixture, the resulting model learned a policy: “If the question is clear, answer it; if it’s vague, ask a pertinent clarification; then answer once clarified.” Impressively, this “assistance model” outperformed a baseline that never asks for clarification, producing more accurate answers on ambiguous questions. Essentially, it was proof that a big model can learn to distinguish when to ask and doing so leads to better outcomes. Notably, they achieved this with relatively few training examples (a few thousand dialogues) and using a behavior cloning approach (supervised learning) rather than complex reinforcement learning. This result aligns with the concept of the “assistance game” in AI safety, which suggests that an AI should collaborate with a human to achieve the human’s goal, querying the human as needed. While the dataset was small, it demonstrated a viable path: train a single model to both ask and answer, by imitating clarified QA dialogs.
• CAMBIGNQ and Clarify-first Pipeline (Seoul, 2023): We mentioned CAMBIGNQ earlier for ambiguity detection, but its main focus is actually on the clarification step. Lee et al. (Findings of EMNLP 2023) constructed CAMBIGNQ by taking thousands of ambiguous questions and, instead of providing multiple answers, they provide a single ideal clarification question for each. These clarification questions were first machine-generated (using InstructGPT) and then manually edited, so they’re quite high quality. For example, if the question was “Who played young Tom Riddle in Harry Potter?”, the clarification question might be: “By ‘young Tom Riddle’, do you mean the young version in Chamber of Secrets or the teenager in Half-Blood Prince?” (with those options explicitly given). The paper then defines a pipeline of three tasks: (1) Ambiguity detection, (2) Clarifying question generation, and (3) Answering based on the user’s clarification. They report baseline results on each (not very high, as we saw, indicating difficulty). This work provided valuable data and metrics – for instance, they emphasize that just listing all possible answers (without asking the user) is not a great user experience on a voice assistant or small screen. It’s better to ask one targeted question to narrow it down. CAMBIGNQ gives researchers a testbed to practice that strategy. By fine-tuning models on task (2) with CAMBIGNQ’s Q&A pairs, you effectively teach them how to phrase a clarification question that presents the main interpretations as options. This is a supervised approach; it doesn’t necessarily ensure the model knows when to ask, but combined with ambiguity detection (task 1), one could trigger the question generation model only for queries flagged as ambiguous.
• Learning from Search Clarification Datasets: There’s also a lineage of work in the information retrieval and conversational search communities (e.g., Qulac, ClariQ datasets) where systems generate clarifying questions for vague search queries. These often involve scenario-specific clarifications like “Did you mean X or Y?” for search intents. While not directly about LLMs, these datasets and methods (many from 2019–2021) laid groundwork that LLM researchers are now building upon. The difference is that today’s LLM-based approaches try to integrate the entire behavior in one model rather than a separate classifier and templated question generator. For instance, older systems might have done: detect ambiguity with a classifier → pick a clarification question from a fixed list or retrieval. Now, with powerful generative models, we can attempt to have the model both identify and spontaneously formulate the question in free form.

The takeaway is that supervised fine-tuning can give an LLM the habit of asking clarifying questions, if you have the right training dialogues. The downside is obtaining those dialogues at scale – it’s expensive to have humans generate a lot of them. Some work bypassed manual writing by using large models to simulate clarifications (as CAMBIGNQ did with InstructGPT, and as others have done by self-chat simulation). But purely supervised approaches might still be limited by the creativity of the data and won’t generalize beyond what they’ve seen.

Rewarding Clarification via Self-Play and RL

Another frontier is using reinforcement learning (RL) or other self-play techniques to actively encourage clarification-seeking behavior. Instead of just copying what humans did, the idea is to define a goal (e.g. successful resolution of the query) and let the model figure out that asking questions is the way to achieve it.

One notable example is STaR-GATE (Andukuri et al., 2024). The name is a mashup of two ideas: GATE (a prior method for active question asking) and STaR (Self-Taught Reasoner, a method where models improve by learning from their own solutions). STaR-GATE’s focus is on a preference elicitation scenario: the user has some hidden preferences (like dietary restrictions in the recipe example), and the model’s job is to ask questions to uncover those, then give a personalized answer.

How do you train something like this? The researchers used a clever self-play loop with three roles: a Questioner (the main model being trained), a Roleplayer (simulated user with a random “persona” describing their preferences), and an Oracle (a model that knows the user’s persona and can produce the ideal answer once preferences are revealed). In training, the Questioner and Roleplayer have a conversation: the Questioner asks a series of questions to the Roleplayer to figure out preferences, then gives a final answer. The Oracle, which has the ground-truth persona, generates the gold standard response for that user. Now here’s the key: they define a reward signal for the Questioner’s questions based on how likely those questions led the Oracle’s answer to be produced. Intuitively, if the Questioner asked the right questions, it should be able to produce an answer that matches the Oracle’s high-quality answer. If it asked irrelevant questions, its final answer would diverge from the ideal. They optimize the Questioner to maximize the probability of the Oracle’s answer (this is done offline, by generating many synthetic conversations and then fine-tuning on the ones with high log-likelihood of the Oracle answer). They also add regularization so the model doesn’t go off-track by asking superfluous questions – it’s encouraged to only ask what’s needed and then stop.

The result: after a couple of iterations of this self-play training, the fine-tuned model greatly improved its questioning strategy. In evaluations, the answers from the STaR-GATE trained model were preferred over the original model’s answers in 72% of cases, when users had hidden preferences. Essentially, by learning to ask the right clarifying questions, the model achieved much higher success in satisfying user needs. This is a big deal – it quantitatively shows that teaching a model to ask questions can make its final responses better (as measured by human preference or a known gold answer).

STaR-GATE is a form of offline reinforcement learning or iterative self-training. It didn’t require human labelers to rank dialogues; instead it leveraged a large language model (GPT-4, in this case) as the Oracle to produce reference answers. One limitation is that this was done in a controlled setting (user preferences were in a predefined list like food taste, etc.), but the approach could potentially be extended to more general ambiguity scenarios.

Another related idea is from the earlier-mentioned Zhang et al. (2024) work on Modeling Future Conversations to Teach Clarifying Questions. Instead of full self-play, they used human annotators in the loop but in a clever way: they had annotators simulate the next turn as well. In their training, an annotator would see an ambiguous question and different AI responses – some which ask for clarification, some which answer directly. The annotator would then role-play the user by providing an answer to the clarifying question (if one was asked) and then see the AI’s final answer. Only after seeing the outcome would the annotator decide which initial response was better. This way, a clarifying question that leads to a correct answer in the next turn would be ranked higher than a direct answer that might have assumed wrong. They call this double-turn preference labeling. By training an RLHF-style model on these augmented preferences, the AI learned to favor responses that set up a successful two-turn interaction. The paper reported a ~5% improvement in final answer accuracy (F1 score) on ambiguous questions, compared to standard training. That’s notable because it shows even a relatively small adjustment in the reward model – evaluating outcomes over two turns instead of one – can shift the policy towards asking clarifying questions more often (and effectively).

Beyond these, there are other imaginative approaches. A recent work by Handa et al. (2024) tried using Optimal Experiment Design methods to pick clarifying questions, treating it like a science experiment: which question would maximize information gain about the user’s intent. They combined this with language models, though in a limited form (pairwise comparisons). Others have looked at offline policy training where an expert model like GPT-4 is used to generate a bunch of user-assistant conversations (some containing clarifications), and then a smaller model is trained on that. In general, the RL and self-play angle is promising because it directly optimizes the outcome (did the clarification help yield a better answer?) rather than just mimicking a script.

However, RL-based methods have to be done carefully – a poorly defined reward could train a model to ask annoying or unnecessary questions. It’s essential to balance when to ask versus when to confidently answer. Ideally, a model should only ask a question if it truly needs to. Some research is examining this “selective clarification” decision. For instance, the CLAM framework (Kuhn et al., 2022) explicitly had two steps: first classify if the question is ambiguous, and only then ask a clarifying question. They showed you could prompt an LLM to do the classification step with a few examples, and it was fairly good at it. The twist was they didn’t fine-tune the model; instead, they basically wrapped a prompting pipeline around it: if ambiguous → ask clarification → get answer → return answer. This is more of a systems approach than a training technique, but it demonstrated that even existing models can perform better if we add a bit of meta-cognition and an extra question-answer turn. In their experiments, adding this clarification step significantly improved answer accuracy on a dataset of ambiguous questions, with only a small increase in conversation length. It also revealed an interesting point: the model rarely got confused by its own clarifying question – meaning it could incorporate the user’s answer and answer correctly, rather than getting thrown off by the dialogue context. This addresses a concern some might have: “if the model wasn’t trained on multi-turn, will it handle the answer to its question properly?” Their results were reassuring on that front.

Encouraging Critical Reasoning and Self-Clarification

In addition to training data and rewards, there’s a more internal approach: equip the model with better reasoning abilities so that it knows when things don’t add up. This often falls under “chain-of-thought” or “self-reflection” research, which isn’t exclusively about clarifying questions but is very relevant.

One idea is to let the model generate an internal chain-of-thought whenever it’s unsure, and possibly even a chain-of-questions. For example, a prompting strategy called Self-Ask (press et al., 2022) lets the model pose a sub-question to itself and answer it, iteratively, before giving a final answer. In ambiguous cases, a model might effectively ask itself “Which interpretation is intended?” and if it can’t decide, that’s a cue that it should ask the user. Some experiments have compared a Self-Ask approach to direct prompting for ambiguity. Kim et al. (APA paper) reported that a naive Self-Ask method (where the model generates an answer and then evaluates if it was ambiguous) wasn’t very effective alone. It seems a model guessing an answer doesn’t necessarily realize post hoc that it should have clarified. More structured reasoning, like explicitly enumerating possible answers or using sampling to detect uncertainty, worked better. For instance, Cole et al. (2023) found that if you sample a model’s answer multiple times and get different answers, that’s a strong sign of ambiguity – they call this “sample repetition” as an uncertainty measure. Such a signal could trigger a clarifying response.

Another sophisticated approach is the Tree-of-Clarifications (ToC) by Gangwoo Kim et al., 2023. Instead of interacting with the user, ToC tries to resolve ambiguity autonomously. It treats an ambiguous question by branching into all plausible interpretations (with the help of retrieved knowledge) and then answering in a long form that covers everything. For example, if the user asks “Which country won the most Olympic medals?”, the ToC system would break this down: does the user mean summer or winter Olympics? Gold medals or total medals? It then finds answers for each combination and writes a summary that addresses each interpretation. This is akin to critical thinking because the model is not taking the question at face value; it’s actively considering alternatives. ToC was shown to outperform previous methods on a benchmark called ASQA (Ambiguous Questions long-form QA). However, one might argue it’s a fallback for when you can’t ask the user – it “bothers the user with clarification” zero times, by answering everything. In practical assistants, we’d likely combine strategies: if it’s feasible, just ask the user; if not (maybe user is unavailable or the system is expected to answer autonomously), then provide a multi-interpretation answer.

Finally, an element of limitations and challenges: asking clarifying questions is not a solved problem. Models might ask too many questions or irrelevant ones if trained improperly. There’s a balance between not enough clarification (leading to wrong answers) and too much clarification (leading to a tedious interrogation of the user). Human data can help teach nuances – for example, to only ask about key uncertainties. Another challenge is evaluation: how do we measure a clarifying question’s quality? It’s not just about semantic correctness; it’s about usefulness to the user. Some works have introduced metrics or human preference tests where success is defined as the user’s issue being resolved after the interaction. That often requires context of the whole dialogue, making automation hard. As a proxy, tasks like AmbigQA or ASQA measure if the final answer matches all possible interpretations or if it matches the interpretation the user actually meant (if that’s known from data). These are complex but important evaluation issues to ensure we’re truly making models better at clarification and not just different.

Toward Human-Aligned Critical Thinkers

The research we’ve surveyed represents a significant push towards more human-aligned AI – models that don’t just parrot information, but engage in a dialogue to understand what the human really wants. By identifying assumptions, asking clarifying questions, and even doing some introspective reasoning, an AI assistant becomes much more reliable and helpful.

To recap the key ideas and progress:

• Identifying Hidden Assumptions: Modern LLMs can be taught to detect ambiguity and questionable assumptions in queries. New datasets (like AmbigQA, CAMBIGNQ, (QA)^2) and taxonomies (CLAMBER) help benchmark this ability. With the right prompts or fine-tuning, models start recognizing when a question is underspecified or loaded, rather than blindly answering. Still, detection accuracy isn’t perfect and often requires careful prompting or fine-tuning.
• Asking Clarifying Questions: Instead of guessing, an aligned model should ask the user for the missing info. Supervised fine-tuning on clarification dialogues (even small datasets) already shows clear gains in answer quality. More advanced methods using RL and self-play (e.g. STaR-GATE, Zhang et al.’s double-turn RLHF) actually reward the model for clarifying when appropriate, leading to big improvements in user satisfaction and task success. The model learns a policy of judicious clarification: ask when needed, don’t when it’s not.
• Critical Thinking and Iterative Refinement: Techniques like chain-of-thought prompting, self-asking, or exploring a tree of possible interpretations all contribute to a model’s critical thinking ability. They encourage the model to analyze the query deeply (almost like how a human expert might brainstorm “What could they mean? Do I have all the info to answer?”). This internal process can either lead to the model deciding to ask the user, or at least lead to a more comprehensive answer. The boundary between “internal reasoning” and “interactive reasoning” is getting blurred – some alignment proposals even have the model effectively hold a conversation with a simulated user or with itself to test an answer before finalizing it. For example, techniques where models critique their own draft answer and then revise have shown promise in reducing hallucinations and catching false assumptions. It’s easy to imagine merging that with user interaction: a model that drafts an answer, realizes it had to assume something, and then instead of giving the flawed answer, turns to the user and asks a clarifying question.

It’s also important to mention limitations and open problems at this frontier:

• When to stop clarifying: A model shouldn’t turn every minor uncertainty into a question. Humans have a sense of what’s reasonable to ask. For instance, if a user asks “Tell me about Python,” it might be ambiguous (Python the language or the snake?), but a savvy assistant might first try to infer from context or give a blended answer (“Python is both a programming language and a snake, I’ll tell you about both briefly…”). Only if the distinction is crucial would it ask, “Do you mean Python the coding language or the animal?”. Training models to have that judgement – essentially a threshold for ambiguity significance – is tricky. Current research often treats ambiguity as binary (ambiguous or not), but in reality there’s a spectrum.
• Quality of clarifying questions: Not any question will do; it must actually resolve the ambiguity. A poorly chosen clarification can confuse users more. For example, asking a very broad question back to the user (“What do you mean?”) is unhelpful; asking a specific, pointed question is better. Some studies found that language models, when asked to clarify, sometimes produce irrelevant or too-generic questions. Ensuring the question targets the key uncertainty is something datasets like CAMBIGNQ explicitly push for (by providing the ideal clarification as a training signal).
• User experience: There’s a user behavior aspect: will users actually answer clarifying questions? One could get annoyed (“Just answer my question!”). This is why ideally the AI also explains why it’s asking. For instance, “To make sure I help you best, could you clarify X?” Maintaining user trust while asking questions is an area of focus in human-computer interaction research, but less so in model training papers. In practice, deployment might require tuning how and when clarifications are presented.
• Generality: Most research to date has siloed on question-answering tasks. But assumption handling is needed in many contexts: task execution (a robot might need to clarify an instruction), dialogue systems, etc. The solutions like RLHF tweaks or synthetic self-play might need to be adapted to those contexts. It’s an open question how well these methods generalize beyond QA-style ambiguities into more complex dialogues with multiple ambiguities or continuous decision making.

In conclusion, the future of truly helpful and reliable AI assistants likely hinges on these kinds of behaviors. Instead of an oracle that may or may not guess what you intended, we want a collaborator that engages in a brief back-and-forth to understand you, then uses its vast knowledge to assist. The research we discussed is moving us in that direction. We’re teaching our language models to be a bit more like good human communicators: when in doubt, ask – and ask smartly. By combining advanced training techniques, novel datasets, and insights from human communication, we are gradually sculpting AI that doesn’t just answer questions, but understands them.

References

• CLAMBER (2024): “Clarifying Ambiguities Benchmark for Language Model Evaluation”
• CAMBIGNQ (Lee et al., EMNLP 2023): “CAMBIGNQ: Clarifying Ambiguous Questions for Next-Generation QA”
• (QA)² (ACL 2023): “Question Answering with Questionable Assumptions”
• APA (Kim et al., 2024): “Aligning Language Models to Explicitly Handle Ambiguity”
• ClarifyingQA (Krasheninnikov et al., 2022): “Clarifying Ambiguous Questions for QA Systems”
• STaR-GATE (Andukuri et al., 2024): “Self-Taught Reasoner with Goal-Aligned Training and Evaluation”
• Double-Turn Preference (Zhang et al., 2024): “Improving Multi-Turn Dialogue Through Reinforcement Learning from Human Feedback”
• Self-Ask (Press et al., 2022): “Measuring and Improving Model Reasoning with Self-Ask”
• Tree-of-Clarifications (Kim et al., 2023): “Structured Clarification Generation in Ambiguous Question Answering”