Low-bit weight-only quantization significantly reduces the memory footprint of large language models (LLMs), but disproportionately affects certain examples.We analyze diverse 3-4 bit methods on LLMs ranging from 7B-70B in size and find that the quantization errors of 50 pairs of methods are strongly correlated (avg.ρ = 0.82) on FineWeb examples.Moreover, the residual stream magnitudes of full-precision models are indicative of future quantization errors.We further establish a hypothesis that relates the residual stream magnitudes to error amplification and accumulation over layers.Using LLM localization techniques, early exiting, and activation patching, we show that examples with large errors rely on precise residual activations in the later layers, and that the outputs of MLP gates play a crucial role in maintaining the perplexity.Our work reveals why certain examples result in large quantization errors and which model components are most critical for performance preservation.

This paper studies in-context learning by decomposing the output of large language models into the individual contributions of attention heads and MLPs (components).We observe curious components: good-performing ones that individually do well on a classification task, even when the full model performs poorly; bad-performing ones that do much worse than chance; and label-biased components that always predict the same label.We find that component accuracies are well-correlated across different demonstration sets and perturbations of prompt templates.Based on our findings, we propose component reweighting, which learns to linearly re-scale the component activations from a few labeled examples.Given 24 labeled examples, our method improves by an average of 6.0% accuracy points over 24-shot ICL across 8 tasks on Llama-2-7B.Overall, this paper both enriches our understanding of ICL and provides a practical method for improvement by examining model internals.Template 1 {text}\nIs this a piece of news regarding World, Sports, Business, or Technology?Template 2 {text} Is this a piece of news regarding World, Sports, Business, or Technology?Correlation = 0.81

Ting-Yun Chang, Jesse Thomason, Robin Jia. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

In-context learning (ICL) enables large language models (LLMs) to perform new tasks by prompting them with a sequence of training examples.However, it is known that ICL is very sensitive to the choice of training examples: randomly sampling examples from a training set leads to high variance in performance.In this paper, we show that carefully curating a subset of training data greatly stabilizes ICL performance without any other changes to the ICL algorithm (e.g., prompt retrieval or calibration).We introduce two methods to choose training subsets-both score training examples individually, then select the highest-scoring ones.CONDACC scores a training example by its average dev-set ICL accuracy when combined with random training examples, while DATAMODELS learns linear regressors that estimate how the presence of each training example influences LLM outputs.Across five tasks and two LLMs, sampling from stable subsets selected by CONDACC and DATAMODELS improves average accuracy over sampling from the entire training set by 7.7% and 6.3%, respectively.Surprisingly, the stable subset examples are not especially diverse in content or low in perplexity, in contrast with other work suggesting that diversity and perplexity are important when prompting LLMs.

Current state-of-the-art vision-and-language models are evaluated on tasks either individually or in a multi-task setting, overlooking the challenges of continually learning (CL) tasks as they arrive. Existing CL benchmarks have facilitated research on task adaptation and mitigating "catastrophic forgetting", but are limited to vision-only and language-only tasks. We present CLiMB, a benchmark to study the challenge of learning multimodal tasks in a CL setting, and to systematically evaluate how upstream continual learning can rapidly generalize to new multimodal and unimodal tasks. CLiMB includes implementations of several CL algorithms and a modified Vision-Language Transformer (ViLT) model that can be deployed on both multimodal and unimodal tasks. We find that common CL methods can help mitigate forgetting during multimodal task learning, but do not enable cross-task knowledge transfer. We envision that CLiMB will facilitate research on a new class of CL algorithms for this challenging multimodal setting.

Pretrained language models have demonstrated outstanding performance in many NLP tasks recently. However, their social intelligence, which requires commonsense reasoning about the current situation and mental states of others, is still developing. Towards improving language models' social intelligence, in this study we focus on the Social IQA dataset, a task requiring social and emotional commonsense reasoning. Building on top of the pretrained RoBERTa and GPT2 models, we propose several architecture variations and extensions, as well as leveraging external commonsense corpora, to optimize the model for Social IQA. Our proposed system achieves competitive results as those top-ranking models on the leaderboard. This work demonstrates the strengths of pretrained language models, and provides viable ways to improve their performance for a particular task.

Ting-Yun Chang, Yun-Nung Chen. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.