Once language models (LMs) are deployed, they can interact with users long-term, ideally evolving based on their feedback. Asking for direct user feedback can be disruptive; thus, we study harvesting implicit user feedback from user-LM interaction logs. We study two user-LM interaction datasets (WildChat and LMSYS). First, we analyze user feedback in the user-LLM conversation logs, providing insights into when and why such feedback occurs. Second, we study harvesting learning signals from such implicit user feedback. Specifically, we study whether incorporating the contents of user feedback (e.g., user wanted clarification), in addition to the polarity of the feedback, can improve the model performance. We observe mixed results, showing this helps in short human-designed questions (MTBench) but not on longer and more complex questions (WildBench). Together, we provide an in-depth study of implicit user feedback, showing its potential and limitations.

In this work, we explore the challenges of developing interactive assistants that resolve ambiguity by asking their users clarifying questions. Specifically, we develop a task-agnostic framework for evaluating a system’s ability to determine when to ask for clarification. Determining when to ask for clarification is a challenging task that requires systems to consider the demands of the individual user (i.e., how much they prioritize speed and usability versus carefulness) and the distribution of interpretations for a given request (i.e., whether an ambiguous request has one dominant, inferable interpretation). Using this framework, we evaluate systems for determining when to clarify across three NLP applications: QA, MT, and NLI. Finally, we introduce present a novel uncertainty estimation approach, IntentSim, that determines the utility of asking a clarifying question by estimating the entropy over user intents. Our method consistently outperforms existing uncertainty estimation approaches at identifying predictions that will benefit from clarification. Furthermore, we find that IntentSim is robust, demonstrating improvements across a wide range of NLP tasks and LMs. Together, our work lays foundation for further studies on clarifying interactions with LM assistants.

Using language models to scalably approximate human preferences on text quality (LLM-as-a-judge) has become a standard practice applicable to many tasks. A judgment is often extracted from the judge’s textual output alone, typically with greedy decoding. However, LLM judges naturally provide distributions over judgment tokens, inviting a breadth of inference methods for extracting fine-grained preferences. We find that taking the mean of the judgment distribution consistently outperforms taking the mode (i.e. greedy decoding) in all evaluation settings (i.e. pointwise, pairwise, and listwise). We further explore novel methods of deriving preferences from judgment distributions, and find that methods incorporating risk aversion often improve performance. Lastly, we analyze LLM-as-a-judge paired with chain-of-thought (CoT) prompting, showing that CoT can collapse the spread of the judgment distribution, often harming performance. Our findings show that leveraging distributional output improves LLM-as-a-judge, as opposed to using the text interface alone.