Source-linked AI summary
CollabLLM: From Passive Responders to Active Collaborators
Shirley Wu, Michel Galley, Baolin Peng, Hao Cheng, Gavin Li, Yao Dou, Weixin Cai, James Zou, Jure Leskovec, Jianfeng Gao
TL;DR
LLMs trained mainly for immediate responses often handle ambiguous requests passively instead of helping users clarify intent and complete goals efficiently. CollabLLM uses collaborative simulation and Multiturn-aware Rewards to estimate long-term response impact and fine-tune more forward-looking collaboration. It achieves 18.5% higher task-specific performance and 46.3% greater interactivity than the best baselines, while increasing user satisfaction by 17.6% and reducing user time by 10.4%.
Problem
LLMs primarily rewarded for immediate responses often prioritize direct answers over clarification, limiting support for users with unclear or evolving intents.
Method
CollabLLM uses collaborative simulation to estimate responses' long-term impact across future turns through Multiturn-aware Rewards combining task success and efficiency.
Results
18.5% higher task-specific performance and 46.3% enhanced interactivity than the best baselines, with 17.6% higher user satisfaction and 10.4% lower user time.
Takeaways & Limitations
CollabLLM advances human-centered LLMs by making them more effective, efficient, engaging, and capable of generalizing across new tasks and interactions.
Takeaways & Limitations
The safety discussion assumes use with safety-aligned LLMs, whose refusal behavior helps constrain unsafe responses.
Abstract
from arXiv · showhide
Large Language Models are typically trained with next-turn rewards, limiting their ability to optimize for long-term interaction. As a result, they often respond passively to ambiguous or open-ended user requests, failing to help users reach their ultimate intents and leading to inefficient conversations. To address these limitations, we introduce CollabLLM, a novel and general training framework that enhances multiturn human-LLM collaboration. Its key innovation is a collaborative simulation that estimates the long-term contribution of responses using Multiturn-aware Rewards. By reinforcement fine-tuning these rewards, CollabLLM goes beyond responding to user requests, and actively uncovers user intent and offers insightful suggestions-a key step towards more human-centered AI. We also devise a multiturn interaction benchmark with three challenging tasks such as document creation. CollabLLM significantly outperforms our baselines with averages of 18.5% higher task performance and 46.3% improved interactivity by LLM judges. Finally, we conduct a large user study with 201 judges, where CollabLLM increases user satisfaction by 17.6% and reduces user spent time by 10.4%.
1. Introduction
Real-world users often have incomplete or evolving intents, while single-turn training encourages LLMs to answer immediately rather than clarify. COLLABLLM addresses this by training forward-looking collaboration and improves task performance, interactivity, satisfaction, and time efficiency.
- Users frequently refine imprecise requests through iterative corrections, which can increase frustration and reduce conversational efficiency.
- Single-turn rewards reduce LLMs’ incentive to seek clarification or help users refine their intents and preferences.
- COLLABLLM estimates responses’ long-term impact through collaborative simulation and Multiturn-aware Rewards, then fine-tunes models to support later task completion and efficiency.
- 18.5% higher task accuracy and 46.3% greater interactivity are achieved on average across three multiturn test sets versus the best baselines.The tasks cover document creation, code generation, and multiturn question answering.
- 17.6% higher user satisfaction and 10.4% average time savings are reported in a study with 201 MTurkers.Qualitative analysis contrasts passive agreement from non-collaborative models with COLLABLLM’s questions and suggestions.
2. Problem Formulation
The paper formulates collaboration as a multiturn conversation in which users pursue implicit goals through alternating user inputs and model responses. The objective is to generate responses that achieve those goals effectively and efficiently.
- A multiturn conversation consists of turns tj = {uj, mj}, where uj is the user input and mj is the model response.The conversation spans K turns and represents a user’s implicit goal g.
- At turn j, the model generates mj from the previous turns t1:j−1 and the current user input uj.
- The model’s objective is to generate a response sequence that achieves goal g, with success evaluated using user satisfaction or external metrics such as LLM-judge accuracy.The overall reward also incorporates user-experience factors such as time cost.
3. Unified Collaborative LLM Training
COLLABLLM aligns training with multiturn objectives by evaluating how responses affect future conversation trajectories. It combines task success with efficiency and engagement, uses scalable simulated forward interactions, and optimizes the resulting rewards with reinforcement or preference-based training.
- Single-turn rewards can favor locally helpful responses that fail to maximize the final reward of a coherent multiturn interaction.For example, isolated article sections may not flow together or remain aligned with the user’s goal.
- Effective collaboration requires responses that consider their impact on the entire conversation trajectory and align with the user’s overarching goal.Asking about maintaining a consistent tone can be preferable to generating an isolated conclusion.
- Multiturn-aware Rewards: COLLABLLM uses collaborative simulation and forward-looking response comparisons to estimate long-term effects and generate responses better aligned with goal g.The approach frames response evaluation in terms of potential subsequent conversations.
- Conversation-level Reward Function: Multiturn-aware Reward combines extrinsic task-success metrics with intrinsic measures of efficiency and engagement.The intrinsic component penalizes excessive tokens and uses an LLM judge to assess user-valued objectives such as interactivity.
- Forward Sampling: A bounded forward-sampling window and a user simulator make future-conversation estimation more scalable than extending every interaction to completion.The window limits sampled future turns, while the simulator produces probabilistic user responses conditioned on conversation history.
- Training: Optimizing Multiturn-aware Rewards with PPO or DPO trains responses that improve overall effectiveness and efficiency by the conversation’s end.The rewards also support synthetic conversation generation for supervised fine-tuning and DPO without human annotation.
4. Experimental Setup*
The experiments use three simulated multiturn tasks spanning document editing, coding assistance, and mathematics, with user-simulator-driven interactions and task-specific plus task-agnostic evaluation. COLLABLLM variants are trained with offline or online fine-tuning procedures and compared with pretrained and proactively prompted baselines.
- Datasets: Three datasets cover collaborative document editing, coding assistance, and multiturn mathematics problem solving.The datasets are built from publicly available data across diverse domains.
- Evaluation environment: The evaluation environment uses GPT-4o-mini to simulate realistic user behaviors across dynamic multiturn interactions.The simulator receives the target problem and conversation history.
- Task metrics: MediumDocEdit-Chat evaluates document similarity with BLEU after iterative feedback and refinements.It samples 100 Medium articles as goal documents.
- Task metrics: BigCodeBench-Chat uses 600 coding problems and evaluates final code with Pass Rate.The task emphasizes clarification and debugging across turns.
- Task metrics: MATH-Chat uses 200 level-5 problems and measures final-solution accuracy with an LLM judge.The interactions address implicit assumptions, intermediate steps, and reasoning clarification.
- Task-agnostic metrics: Evaluation also tracks average token count for efficiency and Interactivity (ITR) with an LLM judge.ITR scores are rescaled to an upper bound of 1.
5. Results of Simulated Experiments
Across simulated multiturn experiments, COLLABLLM improves task performance, conversational efficiency, and interactivity over baselines, while ablations support the value of forward-looking rewards. A case study and zero-shot benchmark further indicate that the model clarifies ambiguous requirements and generalizes collaborative behavior beyond its training tasks.
- Overall results: Proactive Base improves MediumDocEdit-Chat BLEU from 32.2% to 35.0% and reduces read tokens by 0.31k, but its gains remain modest.Its predefined prompting can also trigger unnecessary clarification questions and redundant interactions.
- Overall results: 18.5% higher task-specific performance, 13.3% more efficient conversations, and 46.3% greater interactivity distinguish COLLABLLM from the best baselines.On MediumDocEdit-Chat, Online DPO raises ITR from 0.46 to 0.92; on MATH-Chat, it reduces token count by 1.03k versus the base model.
- Reward ablations: Multiturn-aware rewards consistently improve BLEU, token efficiency, and ITR over immediate reward mechanisms in the MediumDocEdit-Chat ablation.The comparison includes three immediate rewards and three MR variants.
- Reward ablations: Expanding the forward-sampling window generally improves performance and efficiency, while w = 2 balances gains against sampling costs.Immediate rewards remain limited because they ignore long-term impact.
- Behavioral analysis: COLLABLLM clarifies tokenizer, error-handling, and installation preferences instead of assuming preprocessing choices, producing an aligned solution with a 100% Pass Rate.The case study contrasts this behavior with arbitrary lowercase conversion and stopword removal by the base LLM.
- Model generalization: On Abg-CoQA, COLLABLLM asks clarifying questions about 50% of the time while retaining high accuracy on unambiguous inputs.It achieves the highest Macro Accuracy across ambiguous and non-ambiguous sets and improves Macro F1 over the base model, though it remains below GPT-4o on Macro F1.
6. Real-world User Study
The real-world user study evaluates COLLABLLM against Base and Proactive Base in extended document-writing conversations. COLLABLLM achieves stronger document quality, engagement, sustained interaction, and efficiency, while qualitative feedback highlights improved guidance alongside personalization limitations.
- Study Setup: 201 participants completed at least eight-turn document-writing conversations with Base, Proactive Base, or COLLABLLM.Participants wrote blogs, creative pieces, or personal statements and rated interaction every three turns, then assessed document quality and overall experience.
- Quantitative Results: COLLABLLM achieved an average document quality score of 8.50, with 91.4% rating quality good and 56.9% very good.For Base, the corresponding proportions were 88.5% and 39.3%.
- Quantitative Results: 63.8% of participants found COLLABLLM highly engaging, compared with 42.6% for Base.The study also assessed overall interaction experience and repeated interaction ratings during conversations.
- Quantitative Results: COLLABLLM and Proactive Base ratings increased over time, while Base ratings declined during turns 6–9.COLLABLLM consistently received higher average ratings every three turns than Proactive Base.
- Quantitative Results: COLLABLLM reduced time spent by 10.4% versus Base and 15.6% versus Proactive Base.Proactive Base frequently asked unnecessary questions, whereas COLLABLLM provided a more streamlined experience.
- Qualitative Results: Human feedback describes COLLABLLM as engaging and effective at guiding writing and adapting to feedback, but sometimes bland and difficult to personalize.Participants also cited lack of up-to-date information and recommended improvements in personalization, creativity, and real-time knowledge integration.
7. Related Work
Related work improves multiturn interaction through next-turn training, prompting, clarification-question methods, self-chat, and user simulation. COLLABLLM differs by using forward sampling and reward estimation to model long-term effects across diverse, user-centered tasks.
- Non-collaborative LLM Training: Established training frameworks primarily optimize next-turn response quality and do not account for long-term effects on conversation trajectories.This limits their ability to capture effects on intent discovery and long-term task success.
- Multiturn Interaction Methods: Prompting and learning-based methods train models to ask clarification questions or support mixed-initiative dialogue, but often target predefined interaction behaviors.These approaches include supervised fine-tuning, reinforcement learning, direct preference optimization, and active learning.
- Multiturn Interaction Methods: Self-chat and self-play methods learn from observed post-hoc trajectories rather than explicitly modeling how individual responses affect task success.Related methods also often overlook open-ended tasks such as document generation.
- User Simulators: COLLABLLM uses user simulators in forward sampling to account for long-term effects during both offline and online training.The simulator reduces the cost and impracticality of repeatedly sampling conversations with human participants.
- Comparison with Related Work: COLLABLLM is characterized as task-agnostic, versatile in interaction, user-centric, and based on causal, objective-aligned rewards.These dimensions connect its reward estimation to engagement, efficiency, intent discovery, and diverse-domain applicability.
8. Conclusion
The paper argues that LLMs should become active collaborators that uncover user intent in complex multiturn tasks. COLLABLLM uses forward sampling to estimate long-term response effects and demonstrates effectiveness, efficiency, engagement, and generalization in simulated and real-world evaluations.
- Conclusion: COLLABLLM targets real-world multiturn collaboration by making LLMs active collaborators rather than passive responders.The framework focuses on open-ended and complex tasks where models should actively uncover user intents.
- Conclusion: Forward sampling estimates the long-term impact of model responses on conversations.The paper presents this as the key insight for making LLMs more multiturn-aware.
- Conclusion: Simulated and real-world evaluations demonstrate effectiveness, efficiency, engagement, and generalization to new tasks and interactions.The conclusion frames these results as advancing human-centered LLMs.
Impact Statement
The impact statement positions COLLABLLM as a human-centric alternative to evaluations that exclude users and collaborative experience. It also discusses safety implications, emphasizing intent clarification with safety-aligned models and reporting comparable safety to a non-collaborative model.
- Human-Centric Impact: The paper focuses on user- and human-centric AI rather than fully automated tasks without user involvement or collaborative optimization.It links this orientation to concerns about hallucinations, biases, and unsafe language in AI systems.
- Safety Considerations: COLLABLLM’s intent-clarification behavior may create additional opportunities to detect misuse when paired with safety-aligned LLMs.The authors describe this as relevant when malicious users obscure their intentions to bypass safeguards.
- Safety Considerations: Safety experiments report that COLLABLLM performs no worse than an equivalent non-collaboratively trained model in safety.The paper states that all evaluated models are aligned for safety.
- Data Considerations: The study uses Mechanical Turk participants who consented to sharing their writing and were instructed to avoid personally identifiable information.Participants were asked to focus on public-knowledge topics or fictitious stories.
- Future Research: The authors release the code, models, data, benchmarks, and user simulators to support future human-centric research.The release is presented as a way to promote research in this direction.
A.2. Distinction from Other Multiturn Training Frameworks
COLLABLLM differs from trajectory-based training by intervening on individual responses and forward-simulating alternative futures to estimate long-term effects. It also supports MR-guided synthetic data generation and several fine-tuning configurations.
- Observed-trajectory methods assign rewards after conversations finish, learning response preferences from associations with final outcomes.
- COLLABLLM uses forward simulation and causal effect estimation to evaluate counterfactual response impacts and optimize long-term task success.
- MR-guided data generation samples and ranks multiple responses, labeling higher-scoring responses Chosen and lower-scoring responses Rejected.
- The framework can use a user simulator, assistant model, and task-specific metric to generate conversations in simulated environments.
B.3. Full Ablation Results
The ablation compares isolated helpfulness, extrinsic, and intrinsic rewards across window sizes, while the full CollabLLM configuration combines intrinsic and extrinsic signals. The isolated rewards reveal a task-success/interactivity trade-off, whereas the combined setting is balanced.
- Helpfulness alone marginally improves BLEU and ITR but significantly increases token usage at larger window sizes.
- The CollabLLM configuration combines intrinsic and extrinsic rewards through the multiturn-aware formulation and achieves strong, balanced performance.
- Reward type is independent of the multiturn-aware design, allowing different signals to evaluate responses’ long-term impact through forward sampling.
C. Safety Evaluation
The safety evaluation tests COLLABLLM and its Llama-3.1-8B-Instruct baseline on adversarial prompts across four harm categories. Both models are reported as very safe overall, with identical pass rates.
- The evaluation uses adversarial queries covering violence, sexual, self-harm, and hate categories.
- Both Llama-3.1-8B-Instruct and COLLABLLM produced responses with average harm scores mostly in the 0–1 range, indicating very low harm.
- COLLABLLM had slightly lower harm in Sexual and Hate categories but slightly higher harm in the other two categories.
- COLLABLLM produced one unsafe response across 320 evaluations, yielding a 99.7% pass rate identical to the baseline.
- The authors report that collaborative training did not degrade the original model’s safety capabilities without additional safety alignment.
F.2. Analysis: Divergence Between Simulated and Real Users
The paper compares communication patterns between simulated and real users because COLLABLLM is trained exclusively with simulated users. The user study indicates effective generalization while identifying simulator realism and diversity as improvement opportunities.
- User simulators were used exclusively during training because MR computation requires large-scale conversations.
- The user study shows that models trained with simulated users generalize effectively to real users.
- The comparison supports simulator-based training for scalable optimization while revealing opportunities to improve simulator realism and diversity.