DiscoverLLM: From Executing Intents to Discovering Them

AI summary

Overview

Research area: Large language model training and human-centric AI, specifically multi-turn conversational systems, user simulation, and reinforcement learning from simulated interaction. Positioned at the intersection of NLP training methodology, HCI, and cognitive theories of design and writing.

Technical level: Intermediate. The paper builds on familiar LLM training concepts (supervised fine-tuning, DPO, GRPO, LoRA), but its core formalism — an intent-state model with a latent hierarchy and a response-driven state transition function — requires some care to follow.

One-sentence scope: The paper formalizes "intent discovery" as distinct from intent elicitation, introduces a user simulator with a latent hierarchy of progressively concretizing intents that produces a reward signal, and uses that reward to train LLMs that help users form intents they have not yet developed.

What This Paper Is About

Existing conversational LLMs are trained to execute user instructions and to ask clarifying questions when requests are ambiguous. That assumes users already hold a fully formed intent that simply has not been articulated. This paper argues that in open-ended tasks such as writing and design, users are often vague because they have not yet formed an intent at all, so asking "what tone do you want?" cannot work. The goal is to train models that help users discover their intents by adaptively exploring options when intents are unclear and converging on refinement and implementation once intents concretize.

Key Contributions

1. A formalization of intent discovery as a distinct problem. The paper defines a multi-turn conversation where the user's intent state $I_t$ grows monotonically ($I_0 \subseteq I_1 \subseteq \cdots \subseteq I_T$), where a refinement space $\mathcal{R}(I_t)$ holds undiscovered children of discovered intents, and where the user can only articulate intents already in $I_t$ — an expressiveness constraint that makes clarification questions about undiscovered refinements fail.

2. A novel user simulator with an explicit latent intent hierarchy. Intents are represented as tree hierarchies $\mathcal{H}=(V,E)$ (potentially multiple trees ${\mathcal{H}_1, \ldots, \mathcal{H}_K}$ for dimensions such as subject, tone, and structure), with three discovery states per node (undiscovered, emerging, discovered) and both direct and tangential engagement driving state transitions.

3. The DiscoverLLM training framework and reward function. A per-turn reward combines discovery progress $R_d(r_t) = |I_{t+1}| - |I_t|$ with a capped efficiency penalty $R_e(r_t) = -\min(\lambda \cdot \max(0, \text{tokens}(r_t) - \tau), 1)$, supporting SFT, offline RL (DPO), and online RL (PPO or GRPO).

4. Three interactive benchmarks and a human study. The authors construct multi-turn tasks in creative writing, technical writing, and SVG drawing, and validate results with 75 human participants alongside simulated evaluation.

Main Findings

• Intent discovery and satisfaction improve across models and tasks. Applying DiscoverLLM to Llama-3.1-8B-Instruct and Qwen3-8B generally improved discovery and satisfaction while reducing conversation length and increasing interactivity. SFT+DPO generally yielded the best results, with GRPO providing further gains on Qwen3-8B.

• Headline gains. The abstract reports over 10% higher task performance while reducing conversation length by up to 40%. The introduction states intent discovery improved by around 10%, interactivity scores (as rated by LLM judges) increased by 83%, and conversation length dropped by 32%, all compared to the best baselines.

• Relative improvements in Table 1. For Llama on creative writing, the best variant improved Discovery by 11.0%, Satisfaction by 11.3%, ITR by 183%, and reduced tokens by 44.7% (from 3.09k to 1.71k). For Llama on technical writing: -0.0% Discovery, -0.0% Satisfaction, 227% ITR, 11.4% token reduction. For Llama on SVG drawing: 13.2% Discovery, 13.8% Satisfaction, 117% ITR, 27.3% token reduction. For Qwen: creative writing 13.7% / 9.4% / 43.7% / 31.9%; technical writing 14.3% / 7.7% / 26.6% / 31.9%; SVG drawing 4.0% / 9.0% / 1.1% / 40.4%.

• Prompting gave inconsistent gains. For Qwen, prompting raised Discovery from 35.2% to 39.0% and cut conversation length from 3.41k to 3.01k on creative writing. For Llama, prompting hurt — Discovery dropped from 45.6% to 40.0% on SVG drawing. Prompted Llama mostly asked clarifying questions simulated users could not answer, whereas prompted Qwen offered concrete options.

• CollabLLM underperformed in this setting (e.g., 37.3 Discovery on Llama creative writing versus 42.4 for SFT+DPO), which the authors attribute to it being trained primarily to ask clarifying questions.

• SFT alone overfits toward divergence. SFT achieved very high interactivity scores (e.g., 92.3 for Llama on creative writing) but did not balance divergence with convergence. A behavioral analysis of Qwen variants on creative writing found the base model was almost entirely convergent (91% CCC trigrams), SFT overfit toward divergence (43% DDD), and DiscoverLLM variants were more balanced — with SFT+DPO+GRPO, the highest-Discovery variant, showing the most balanced patterns.

• Efficiency gains at matched performance. SFT+DPO on Llama in technical writing matched performance with 11.4% fewer tokens.

• Intent satisfaction remains hard overall. The authors note that fully satisfying intents within five turns is difficult when intents are unformed at the outset, and that limited diversity in LLM outputs compounds the problem. Excessive diversity can also hurt, as seen with structural conventions in technical writing.

• Generalization to unseen tasks. Evaluated on travel plans, data visualization code, research abstracts, website components, and text-to-image prompts (N=50, 10 artifacts per task), Llama variants showed 11.9% relative Discovery improvement with only a 0.9% token cost.

• Robustness across simulator configurations. Four ablations — two different backbone LLMs (GPT-5.4-mini, Claude Haiku 4.5), removing single-dimension focus, and tripling the tangential probability — all showed DiscoverLLM achieving the best Discovery Score with the fewest tokens. DiscoverLLM also matched or exceeded all baselines on the CollabLLM user simulator and on AmbigNQ.

• Human study results. With 75 Prolific participants interacting for at least 8 turns, 84% of DiscoverLLM participants rated interaction satisfaction 8 or higher, versus 80% for Base and 72% for Prompted Base. DiscoverLLM matched or exceeded baselines on time spent, with notably lower variance, and reached higher satisfaction early (turn 3) and maintained it.

• Assumptions largely held. Of 75 annotated conversations, only 4 (5.3%) contained a genuine preference reversal.

• No observed safety degradation. A small-scale safety evaluation on established benchmarks showed no significant degradation between base models and their DiscoverLLM variants.

Methodology in Plain English

The core obstacle is that you cannot observe a real user's unformed intents, so you have nothing to compute a training reward from. The authors solve this with a simulator.

First, they build an intent tree for each task artifact. Starting from existing artifacts (such as Reddit r/WritingPrompts posts, journalistic articles, and SVG drawings), an LLM lists every requirement the artifact satisfies — these are the most concrete intents. The LLM then iteratively abstracts that list across several levels, and finally organizes all the intents into a tree where abstract intents are parents of more specific ones.

Second, this tree drives a simulated user. The user starts with only a few abstract intents discovered and writes an initial request based on them. At each turn, the simulator checks how the model's response engages with undiscovered children of discovered intents. A direct hit — explicitly asking about or satisfying an intent — marks it discovered immediately. A tangential hit — offering related but non-matching options — accumulates toward a threshold, which can advance a node from undiscovered to emerging to discovered. Emerging intents can only be vaguely referenced, and undiscovered intents cannot be articulated at all, reproducing the "I'm not sure, something different" behavior of real users.

Third, the simulator produces a reward each turn: plus one for every newly discovered intent, minus a capped penalty for responses that exceed a token threshold. Discovery is weighted first, efficiency second, because the authors found that normalizing by remaining intents produced unstable signals.

Finally, models (Llama-3.1-8B-Instruct and Qwen3-8B, tuned with LoRA) are trained on this reward in several ways: supervised fine-tuning on selected synthetic conversations, offline DPO on turn-level preference pairs, DPO on top of the SFT model, and GRPO on top of SFT+DPO for Qwen.

Evaluation uses four metrics: an Intent Discovery Score normalized against per-instance bounds (excluding intents discovered by all models or none), an Intent Satisfaction Score judged by GPT-5.1 over leaf nodes, an Interactivity Score (also GPT-5.1, rescaled to 0-1), and average token count. Datasets use 500 training artifacts and 100 evaluation artifacts per domain; each conversation runs 5 turns and is repeated 3 times during evaluation with averaged results. Intent trees are built with Claude Sonnet 4.5 and the user simulator runs on Gemini 3 Flash.

Why This Matters

Impact on research. The paper reframes a common assumption in LLM training. Benchmarks largely assess single-turn performance, and RLHF-style fine-tuning rewards single-turn full outputs, both implicitly treating the user's request as complete. Work on multi-turn collaboration typically assumes a hidden-but-formed intent to be surfaced by clarification. This paper argues for a third category — intents that do not exist yet and must be discovered — and supplies both a formalism and a training pipeline for it. It also contributes a user simulator design whose internal cognitive state is explicit rather than a hidden fixed goal, which could be reused for training and evaluation beyond this paper's domains.

Real-world applications:

• Writing assistants. Creative writing, technical writing, and personal essays, where the paper shows both simulated gains and higher human satisfaction.
• Visual and design tools. SVG drawing is a studied domain, and other evaluated tasks include website components and text-to-image prompts.
• Planning and research assistance. The generalization experiments cover travel plans, research abstracts, and data visualization code.
• Ambiguity-heavy question answering. Evaluations on AmbigNQ and the CollabLLM user simulator test clarification capability directly.

Industry relevance. Products that deploy conversational assistants for open-ended creation face exactly the failure mode this paper targets: a user gives a broad request, the system returns a polished artifact, and the user reacts with vague dissatisfaction. The paper's efficiency results are also commercially salient — the best variants frequently reduce token usage substantially against the best baselines (up to 44.7% for Llama on creative writing), and interactivity improvements mean less back-and-forth. The authors also position the work against fully autonomous agents, arguing that exploring in small steps with the user preserves user agency and avoids fixation on early high-fidelity outputs.

Future Directions

• Relaxing the formalization's assumptions. The authors explicitly state that their model assumes monotonic refinement (once discovered, intents stay discovered, ignoring backtracking) and that intent discovery depends solely on assistant responses, excluding external factors. They also note that a user may construct intents entirely through interaction rather than discovering pre-existing ones — modeling construction rather than discovery remains future work.
• Improving the diversity of generated options. Both the LLM-as-judge analysis and users in the study flagged that generated options were sometimes "overwhelming" and "a bit standardised." The authors identify enhancing exploration diversity as a needed direction, noting that limited LLM output diversity made divergence less useful.
• More comprehensive safety analysis. Because models trained to explore user intents may be more susceptible to engaging with vague or harmful requests, the authors call for broader safety analyses with diverse scenarios and attacks before deployment, beyond their own small-scale evaluation.
• Better simulators and broader validation. The authors acknowledge that LLM-based simulators may not capture real human diversity, and that their human study is a limited sample.

Target Audience

Researchers and practitioners in LLM training, multi-turn dialogue, and human-centered AI will get the most from this paper, particularly those working on reward design, user simulation, or reinforcement learning from simulated interaction. HCI researchers studying creativity support tools and open-ended design will find the intent-hierarchy formalization and the user study directly relevant. Product teams building conversational assistants for writing, design, or planning are the practical audience, since the paper's core claim concerns a failure mode those teams encounter routinely. Readers need comfort with standard fine-tuning terminology (SFT, DPO, GRPO, LoRA) to follow the training section, but the problem framing and user study are accessible without it.

Authors’ abstract

To handle ambiguous and open-ended requests, Large Language Models (LLMs) are increasingly trained to interact with users to surface intents they have not yet expressed (e.g., ask clarification questions). However, users are often ambiguous because they have not yet formed their intents: they must observe and explore outcomes to discover what they want. Simply asking "what kind of tone do you want?" fails when users themselves do not know. We introduce DiscoverLLM, a novel and generalizable framework that trains LLMs to help users form and discover their intents. Central to our approach is a novel user simulator that models cognitive state with a hierarchy of intents that progressively concretize as the model surfaces relevant options -- where the degree of concretization serves as a reward signal that models can be trained to optimize. Resulting models learn to collaborate with users by adaptively diverging (i.e., explore options) when intents are unclear, and converging (i.e., refine and implement) when intents concretize. Across proposed interactive benchmarks in creative writing, technical writing, and SVG drawing, DiscoverLLM achieves over 10% higher task performance while reducing conversation length by up to 40%. In a user study with 75 human participants, DiscoverLLM improved conversation satisfaction and efficiency compared to baselines.