Hugging Face Trending Papers
Investigating Knowledge Transfer Across Interactive Dialogue Games
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Hugging Face Trending Papers
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Skill Issue: Are Skills Language-Invariant in LLMs?
Large language models access knowledge inconsistently across languages, but to what extent do they differ in their skill sets when interacting with different languages? This work quantifies cross-ling...
arXiv Machine Learning
5d ago
Skill Issue: Are Skills Language-Invariant in LLMs?
The paper investigates whether large language models (LLMs) exhibit language‑specific skill differences by having two identical model instances play a text‑based game in different languages. Using a multilingual extension of TextArena, the authors evaluate three open‑weight models across eight languages and six games, finding that the same model can show markedly different performance—varying win–loss margins, invalid actions, and strategic choices—depending on the language interface. Analyses pinpoint language‑specific failures in spatial reasoning, card‑conditioned decisions, and optimal move selection, and demonstrate that adjusting the intermediate reasoning language can recover much of the lost performance.
arXiv Machine Learning
Jul 31
Strategy, Not Payoffs: A Behavioural Embedding of Normal-Form Games
arXiv:2607. 27536v1 Announce Type: cross Abstract: Learning a strategic task changes more than what is directly taught: fine-tuning on one game can either enhance or degrade an agent's ability to reason in another.
arXiv AI
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A Survey on Large Language Model-Based Game Agents
arXiv:2404. 02039v5 Announce Type: replace Abstract: Game environments provide rich, controllable settings that stimulate many aspects of real-world complexity.
arXiv Machine Learning
1d ago
Acquire, Repair, Preserve: A Diagnosis-Guided Post-Training Recipe for Small-Model Dialogue Game Agents
The paper presents a post‑training recipe for small dialogue‑game agents that involves three steps: acquiring broad game participation via supervised fine‑tuning, repairing specific local failures with turn‑local preference pairs, and preserving general capabilities. Applied to the LM Playschool Challenge, the method raises the public clemscore from 10.67 to 38.92 and the closed in‑domain score from 13.41 to 41.17 while keeping overall static performance nearly unchanged. The gains are mainly within the targeted game family, with limited improvement on out‑of‑domain clemscore.
arXiv AI
Aug 21
Break It Down, Pass It On: Cross-Task Skill Transfer in LLM Agents
arXiv:2608. 20274v1 Announce Type: new Abstract: Large language model (LLM) agents can induce skills from completed tasks and reuse them later to grow more capable with experience.