17 citations · 42 across the 45 of their papers we have counts for
5 papers · 1 filter
Chopthin-Consensus Power Sampling: A Diversity-Preserving Approach to LLM Decoding
Minoo Ahmadi, Seyedarmin Azizi, Erfan Baghaei Potraghloo +2
Inference-time power sampling via Sequential Monte Carlo (SMC) can substantially improve large language model (LLM) reasoning without requiring post-training. However, many existin…
COFT: Counterfactual-Conformal Decoding for Fair Chain-of-Thought Reasoning in Large Language Models
Arya Fayyazi, Mehdi Kamal, Massoud Pedram
Large language models (LLMs) can reveal and amplify societal biases during chain-of-thought (CoT) generation. We present COFT (Chain of Fair Thought), a training-free decoding meth…
One Token Away from Collapse: The Fragility of Instruction-Tuned Helpfulness
Erfan Baghaei Potraghloo, Seyedarmin Azizi, Souvik Kundu +1
Instruction-tuned large language models produce helpful, structured responses, but how robust is this helpfulness under trivial constraints? We show that simple lexical constraints…
Top-H Decoding: Adapting the Creativity and Coherence with Bounded Entropy in Text Generation
Erfan Baghaei Potraghloo, Seyedarmin Azizi, Souvik Kundu +1
Large language models (LLMs), despite their impressive performance across a wide range of tasks, often struggle to balance two competing objectives in open-ended text generation: f…
LaMDA: Large Model Fine-Tuning via Spectrally Decomposed Low-Dimensional Adaptation
Seyedarmin Azizi, Souvik Kundu, Massoud Pedram
Low-rank adaptation (LoRA) has become the default approach to fine-tune large language models (LLMs) due to its significant reduction in trainable parameters. However, trainable pa…