The paper introduces LLM‑PeerReview, an unsupervised ensemble method that selects the best response from multiple LLM-generated candidates by scoring each answer with several LLMs, aggregating those scores via averaging or a graphical model, and choosing the highest-scoring response. The approach is peer‑review inspired, transparent, and interpretable, and it outperforms the Smoothie‑Global model by 6.9%–7.3% across factual recall QA, math reasoning, and instruction‑following tasks. The authors also provide a curated benchmark suite of 12 ensemble methods evaluated on four datasets and three task families to aid reproducibility.
arXiv:2607. 27783v1 Announce Type: cross Abstract: Large Language Models (LLMs) explore problems through chain-of-thought, but this exploration is buried in unstructured prose.
arXiv:2604. 11996v2 Announce Type: replace-cross Abstract: Should we trust Large Language Models (LLMs) with high accuracy?
arXiv:2610.19165v1 Announce Type: cross Abstract: Many real-world questions demand not a single fact but an organized collection of them, yet existing factual knowledge benchmarks almost exclusively...
arXiv:2607. 05992v1 Announce Type: cross Abstract: Mathematical reasoning has become a central task for evaluating and tuning reasoning Large Language Models (LLMs), yet existing benchmarks remain heavily biased toward high-resource languages, with English and Chinese dominating both pre-training corpora and evaluation suites.
arXiv:2606. 27226v1 Announce Type: new Abstract: Evaluating LLM outputs remains a major bottleneck in NLP: human evaluation is expensive and slow, lexical metrics correlate poorly with human judgments on open-ended generation, and holistic LLM judges often produce opaque scores that are hard to debug.
Evaluating LLM outputs remains a major bottleneck in NLP: human evaluation is expensive and slow, lexical metrics correlate poorly with human judgments on open-ended generation, and holistic LLM judges often produce opaque scores that are hard to debug. We propose BINEVAL, a framework that decomposes evaluation criteria into atomic binary questions and aggregates the resulting verdicts into interpretable, multi-dimensional scores.
The paper introduces the concept of LLM‑specific utility, defining it as the performance gain a target large language model (LLM) achieves when provided with a passage compared to answering without evidence. A benchmark of utilitarian passages is built for four LLMs (Qwen3‑8B/14B/32B and Llama 3.1‑8B) across three QA datasets, revealing that each model benefits most from its own tailored evidence and that evidence optimized for other models is consistently suboptimal. The authors also create SpecUBench, a benchmark for LLM‑specific utility judgment, and show that current utility‑aware retrieval methods largely capture model‑agnostic usefulness, struggling to estimate LLM‑specific utility. "whyItMatters":"The study demonstrates that retrieval‑augmented generation must consider model‑specific evidence selection to truly improve LLM performance, highlighting a gap in existing utility‑aware methods."
IDEAlign introduces a new protocol for evaluating the similarity of large language model (LLM) annotations to expert judgments. It uses pick‑the‑odd‑one‑out tasks to capture expert similarity and benchmarks various similarity methods—including text embeddings, topic models, and LLM-as-a-judge—against these human ratings. Applied to educational datasets, the study finds that most metrics miss nuanced expert dimensions, with LLM-as-a-judge performing best yet still insufficient for full expert alignment.
The paper introduces a multi-agent peer‑reviewed reasoning approach for medical question answering, where several large language models independently generate chain‑of‑thought explanations and then evaluate each other’s reasoning for factual correctness and logical soundness. The highest‑rated reasoning chain is used to produce the final answer. Experiments with five state‑of‑the‑art LLMs on three benchmark datasets show that this method consistently outperforms single‑model chain‑of‑thought reasoning and majority voting ensembles, achieving an average accuracy of 0.820 versus 0.777 for the best single model.
arXiv:2602. 12424v2 Announce Type: replace-cross Abstract: Benchmarks establish a standardized evaluation framework to systematically assess the performance of large language models (LLMs), facilitating objective comparisons and driving advancements in the field.
arXiv:2603.00889v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have exhibited remarkable reasoning capabilities, largely enabled by supervised fine-tuning (SFT)- and reinforce...
arXiv:2606. 15419v1 Announce Type: cross Abstract: Objective: To enhance the accuracy, interpretability, and robustness of large language models (LLMs) in medical question answering (MedQA).