Rate, Explain and Cite (REC): Enhanced Explanation and Attribution in Automatic Evaluation by Large Language Models

LLMs have demonstrated impressive proficiency in generating coherent and high-quality text, making them valuable across a range of text-generation tasks. However, rigorous evaluation of this generated content is crucial, as ensuring its quality remains a significant challenge due to persistent issues such as factual inaccuracies and hallucinations. This paper introduces two fine-tuned general-purpose LLM autoevaluators, REC-12B and REC-70B, specifically designed to evaluate generated text across several dimensions: faithfulness, instruction following, coherence, and completeness. These models not only provide ratings for these metrics but also offer detailed explanations and verifiable citations, thereby enhancing trust in the content. Moreover, the models support various citation modes, accommodating different requirements for latency and granularity. Extensive evaluations on diverse benchmarks demonstrate that our general-purpose LLM auto-evaluator, REC-70B, outperforms state-of-the-art LLMs, excelling in content evaluation by delivering better quality explanations and citations with minimal bias. It achieves Rank \#1 as a generative model on the RewardBench leaderboard\footnote{\url{https://huggingface.co/spaces/allenai/reward-bench}} under the model name \texttt{TextEval-Llama3.1-70B}. Our REC dataset and models are released at \url{https://github.com/adelaidehsu/REC}.

Mechanistic Interpretation through Contextual Decomposition in Transformers

Transformers exhibit impressive capabilities but are often regarded as black boxes due to challenges in understanding the complex nonlinear relationships between features. Interpreting machine learning models is of paramount importance to mitigate risks, and mechanistic interpretability is in particular of current interest as it opens up a window for guiding manual modifications and reverse-engineering solutions. In this work, we introduce contextual decomposition for transformers (CD-T), extending a prior work on CD for RNNs and CNNs, to address mechanistic interpretation computationally efficiently. CD-T is a flexible interpretation method for transformers. It can capture contributions of combinations of input features or source internal components (e.g. attention heads, feed-forward networks) to (1) final predictions or (2) the output of any target internal component. Using CD-T, we propose a novel algorithm for circuit discovery. On a real-world pathology report classification task: we show CD-T distills a more faithful circuit of attention heads with improved computational efficiency (speed up 2x) than a prior benchmark, path patching. As a versatile interpretation method, CD-T also exhibits exceptional capabilities for local interpretations. CD-T is shown to reliably find words and phrases of contrasting sentiment/topic on SST-2 and AGNews datasets. Through human experiments, we demonstrate CD-T enables users to identify the more accurate of two models and to better trust a model's outputs compared to alternative interpretation methods such as SHAP and LIME.

An Investigation into the Effects of Pre-training Data Distributions for Pathology Report Classification

Pre-trained transformer models have demonstrated success across many natural language processing (NLP) tasks. In applying these models to the clinical domain, a prevailing assumption is that pre-training language models from scratch on large-scale biomedical data results in substantial improvements. We test this assumption with 4 pathology classification tasks on a corpus of 2907 prostate cancer pathology reports. We evaluate 5 transformer pre-trained models that are the same size but differ in pre-training corpora. Specifically, we analyze 3 categories of models: 1)General-domain: BERT and Turing Natural Language Representation (TNLR) models, which use general corpora for pre-training, 2)Mixed-domain: BioBERT which is obtained from BERT by including PubMed abstracts in pre-training and Clinical BioBERT which additionally includes MIMIC-III clinical notes and 3)Domain-specific: PubMedBERT which is pre-trained from scratch on PubMed abstracts. We find the mixed-domain and domain-specific models exhibit faster feature disambiguation during fine-tuning. However, the domain-specific model, PubMedBERT, can overfit to minority classes when presented with class imbalance, a common scenario in pathology report data. At the same time, the mixed-domain models are more resistant to overfitting. Our findings indicate that the use of general natural language and domain-specific corpora in pre-training serve complementary purposes for pathology report classification. The first enables resistance to overfitting when fine-tuning on an imbalanced dataset while the second allows for more accurate modelling of the fine-tuning domain. An expert evaluation is also conducted to reveal common outlier modes of each model. Our results could inform better fine-tuning practices in the clinical domain, to possibly leverage the benefits of mixed-domain models for imbalanced downstream datasets.

Explaining black box text modules in natural language with language models

Large language models (LLMs) have demonstrated remarkable prediction performance for a growing array of tasks. However, their rapid proliferation and increasing opaqueness have created a growing need for interpretability. Here, we ask whether we can automatically obtain natural language explanations for black box text modules. A "text module" is any function that maps text to a scalar continuous value, such as a submodule within an LLM or a fitted model of a brain region. "Black box" indicates that we only have access to the module's inputs/outputs. We introduce Summarize and Score (SASC), a method that takes in a text module and returns a natural language explanation of the module's selectivity along with a score for how reliable the explanation is. We study SASC in 3 contexts. First, we evaluate SASC on synthetic modules and find that it often recovers ground truth explanations. Second, we use SASC to explain modules found within a pre-trained BERT model, enabling inspection of the model's internals. Finally, we show that SASC can generate explanations for the response of individual fMRI voxels to language stimuli, with potential applications to fine-grained brain mapping. All code for using SASC and reproducing results is made available on Github.