Human Feedback and Eval Paper Explorer

Match reason: Keyword overlap 2/2 across title and protocol fields.

• In human evaluations conducted by native speakers, Voxtral TTS is preferred for multilingual voice cloning due to its naturalness and expressivity, achieving a 68.4\% win rate over ElevenLabs Flash v2.5.

Match reason: Keyword overlap 1/2 across title and protocol fields.

• Owing to its conciseness and obscurity, classical Chinese can partially bypass existing safety constraints, exposing notable vulnerabilities in LLMs.
• To enhance readability and evaluation accuracy, we further design a classical Chinese to English translation module.

Match reason: Keyword overlap 1/2 across title and protocol fields.

• WinoMTeus adapts the WinoMT benchmark to examine how gender-neutral Basque occupations are translated into gendered languages such as Spanish and French.
• FLORES+Gender, in turn, extends the FLORES+ benchmark to assess whether translation quality varies when translating from gendered languages (Spanish and English) into Basque depending on the gender of the referent.

Match reason: Keyword overlap 1/2 across title and protocol fields.

• We subsequently apply supervised fine-tuning, preference optimization, and chat vector merging to introduce robust instruction-following behavior.
• Evaluation on a comprehensive suite of Estonian benchmarks shows consistent gains in linguistic competence, knowledge, reasoning, translation quality, and instruction-following compared to the original base model and its instruction-tuned…

Match reason: Keyword overlap 1/2 across title and protocol fields.

• Our benchmark is built on a user manual for an agricultural machine, available in English, French, and German.
• The evaluation focuses on realistic "needle-in-a-haystack" challenges and includes unanswerable questions to test for hallucinations.

Match reason: Matched by broad semantic/index fallback.

• To investigate this potential capability gap, we present CricBench, a comprehensive benchmark suite for evaluating LLMs on specialized cricket data.
• We evaluate six state-of-the-art models, including GPT-4o, Claude 3.7 Sonnet, and open-source models, using a strict evaluation protocol.

Match reason: Matched by broad semantic/index fallback.

Match reason: Matched by broad semantic/index fallback.

• Computer-using agents (CUAs) are becoming increasingly capable; however, it remains difficult to scale evaluation of whether a trajectory truly fulfills a user instruction.
• In this work, we study reward modeling from execution video: a sequence of keyframes from an agent trajectory that is independent of the agent's internal reasoning or actions.

Match reason: Matched by broad semantic/index fallback.

• Analysis shows that, by enabling on-policy thinking language selection as an action during RL, ExpLang effectively extends the RL exploration space with diversified language preference and improves the RL exploitation outcome with leveraged…

Match reason: Matched by broad semantic/index fallback.

• We present a comprehensive set of experiments for Basque that systematically study different combinations of these components evaluated on benchmarks and human preferences from 1,680 participants.
• We release code, models, instruction datasets, and human preferences to support full reproducibility in future research on low-resource language adaptation.