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Interpreting the Latent Structure of Operator Precedence in Language Models
Dharunish Yugeswardeenoo, Harshil Nukala, Ved Shah +4
Large Language Models (LLMs) have demonstrated impressive reasoning capabilities but continue to struggle with arithmetic tasks. Prior works largely focus on outputs or prompting s…
ERGO: Entropy-guided Resetting for Generation Optimization in Multi-turn Language Models
Haziq Mohammad Khalid, Athikash Jeyaganthan, Timothy Do +4
Large Language Models (LLMs) suffer significant performance degradation in multi-turn conversations when information is presented incrementally. Given that multi-turn conversations…
Adaptive Originality Filtering: Rejection Based Prompting and RiddleScore for Culturally Grounded Multilingual Riddle Generation
Duy Le, Kent Ziti, Evan Girard-Sun +4
Language models are increasingly tested on multilingual creativity, demanding culturally grounded, abstract generations. Standard prompting methods often produce repetitive or shal…
Semantic Convergence: Investigating Shared Representations Across Scaled LLMs
Daniel Son, Sanjana Rathore, Andrew Rufail +6
We investigate feature universality in Gemma-2 language models (Gemma-2-2B and Gemma-2-9B), asking whether models with a four-fold difference in scale still converge on comparable…
Rewrite-to-Rank: Optimizing Ad Visibility via Retrieval-Aware Text Rewriting
Chloe Ho, Ishneet Sukhvinder Singh, Diya Sharma +4
Search algorithms and user query relevance have given LLMs the ability to return relevant information, but the effect of content phrasing on ad visibility remains underexplored. We…
Causal Language Control in Multilingual Transformers via Sparse Feature Steering
Cheng-Ting Chou, George Liu, Jessica Sun +4
Deterministically controlling the target generation language of large multilingual language models (LLMs) remains a fundamental challenge, particularly in zero-shot settings where…
Rosetta-PL: Propositional Logic as a Benchmark for Large Language Model Reasoning
Shaun Baek, Shaun Esua-Mensah, Cyrus Tsui +6
Large Language Models (LLMs) are primarily trained on high-resource natural languages, limiting their effectiveness in low-resource settings and in tasks requiring deep logical rea…