Human Feedback and Eval Paper Explorer

Match reason: Keyword overlap 1/1 across title and protocol fields.

• Large language model (LLM) agents are moving beyond prompting alone.
• ChatGPT marked the rise of general-purpose LLM assistants, DeepSeek showed that on-policy reinforcement learning with verifiable rewards can improve reasoning and tool use, and OpenClaw highlights a newer direction in which agents…

Match reason: Keyword overlap 1/1 across title and protocol fields.

• Despite the advances in PEFT methods, current evaluations remain limited (in terms of evaluated models and datasets) and difficult to reproduce.
• To bridge this gap, we introduce PEFT-Bench, a unified end-to-end benchmark for evaluating diverse PEFT methods on autoregressive LLMs.

Match reason: Keyword overlap 1/1 across title and protocol fields.

• Realistic user simulation is crucial for training and evaluating multi-turn dialogue systems, yet creating simulators that accurately replicate human behavior remains a significant challenge.
• An effective simulator must expose the failure modes of the systems under evaluation.

Match reason: Keyword overlap 1/1 across title and protocol fields.

• Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.

Match reason: Keyword overlap 1/1 across title and protocol fields.

• Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.

Match reason: Keyword overlap 1/1 across title and protocol fields.

• QSTN enables robust evaluation of questionnaire presentation, prompt perturbations, and response generation methods.
• Our extensive evaluation (>40 million survey responses) shows that question structure and response generation methods have a significant impact on the alignment of generated survey responses with human answers.

Match reason: Keyword overlap 1/1 across title and protocol fields.

• Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.

Match reason: Keyword overlap 1/1 across title and protocol fields.

• Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.

Match reason: Keyword overlap 1/1 across title and protocol fields.

• Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.

Match reason: Keyword overlap 1/1 across title and protocol fields.

• Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.

Match reason: Keyword overlap 1/1 across title and protocol fields.

• Extensive experiments on the AerialVLN and OpenFly benchmark validate the effectiveness of our method.

Match reason: Keyword overlap 1/1 across title and protocol fields.

• Across mathematical reasoning (GSM8K, MATH-500, AIME24), procedural generation (LongProc), conversational long-memory benchmarks (LongMemEval), and long-context understanding (LongBenchV2 and SCBench), TRIM-KV consistently outperforms…
• Qualitative analyses further reveal that learned retention scores align with human intuition, naturally recovering heuristics such as sink tokens, sliding windows, and gist compression without explicit design.

Match reason: Keyword overlap 1/1 across title and protocol fields.

Match reason: Keyword overlap 1/1 across title and protocol fields.

• We present PRISM, a training-free framework that integrates refined system prompting, in-context learning (ICL), and lightweight multi-agent coordination for document and chunk ranking tasks.
• Our primary contribution is a systematic empirical study of when each component provides value: prompt engineering delivers consistent performance with minimal overhead, ICL enhances reasoning for complex queries when applied selectively,…

Match reason: Keyword overlap 1/1 across title and protocol fields.

• Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.

Match reason: Matched by broad semantic/index fallback.

• Experimental results demonstrate that our proposed metric identifies redundancy in LLM outputs, offering a practical tool for automated evaluation of response brevity in conversational AI systems without the need for ground truth human…

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• Vision-Language Models (VLMs), such as CLIP, have achieved impressive zero-shot recognition performance but remain highly susceptible to adversarial perturbations, posing significant risks in safety-critical scenarios.
• Comprehensive experiments on diverse CLIP backbones and fine-grained benchmarks show that TTP consistently surpasses state-of-the-art test-time defenses, delivering substantial improvements in adversarial robustness without compromising…

Match reason: Matched by broad semantic/index fallback.

• Abstract shows limited direct human-feedback or evaluation-protocol detail; use as adjacent methodological context.

Match reason: Matched by broad semantic/index fallback.

• Deep learning methods could assist pathologists by extracting diagnostic information from routinely available HE-stained slides directly, yet comprehensive benchmarks for lymphoma subtyping on multicenter data are lacking.
• In this work, we present the first multicenter lymphoma benchmark, covering four common lymphoma subtypes and healthy control tissue.

Match reason: Matched by broad semantic/index fallback.

• Real-world indicators play an important role in many natural language processing (NLP) applications, such as life-event for mental health analysis and risky behaviour for online safety, yet labelling such information in training datasets is…
• We introduce two new expert-annotated resources: A mental health Reddit well-being dataset and an online safety Facebook sharenting risk dataset.