3 citations · 5 across the 11 of their papers we have counts for
5 papers · 1 filter
Benchmark Inflation: Revealing LLM Performance Gaps Using Retro-Holdouts
Jacob Haimes, Cenny Wenner, Kunvar Thaman +4
The training data for many Large Language Models (LLMs) is contaminated with test data. This means that public benchmarks used to assess LLMs are compromised, suggesting a performa…
Towards Interpreting Visual Information Processing in Vision-Language Models
Clement Neo, Luke Ong, Philip Torr +3
Vision-Language Models (VLMs) are powerful tools for processing and understanding text and images. We study the processing of visual tokens in the language model component of LLaVA…
Turning Up the Heat: Min-p Sampling for Creative and Coherent LLM Outputs
Minh Nhat Nguyen, Andrew Baker, Clement Neo +3
Large Language Models (LLMs) generate text by sampling the next token from a probability distribution over the vocabulary at each decoding step. Popular sampling methods like top-p…
Interpreting Context Look-ups in Transformers: Investigating Attention-MLP Interactions
Clement Neo, Shay B. Cohen, Fazl Barez
Understanding the inner workings of large language models (LLMs) is crucial for advancing their theoretical foundations and real-world applications. While the attention mechanism a…
Understanding Addition and Subtraction in Transformers
Philip Quirke, Clement Neo, Fazl Barez
We use integer addition and subtraction as a controlled, exactly-solvable testbed for what can be said with confidence about the algorithm a low-loss transformer implements - logic…