mtybilly/Instruct2DS huggingface dataset:
Talking the Talk Does Not Entail Walking the Walk: On the Limits of Large Language Models in Lexical Entailment Recognition
Authors: Lucio La Cava, Andrea Tagarelli Candida Maria Greco
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Verbs form the backbone of language, providing the structure and meaning to sentences. Yet, their intricate semantic nuances pose a longstanding challenge. Understanding verb relations through the concept of lexical entailment is crucial for comprehending sentence meanings and grasping verb dynamics. This work investigates the capabilities of eight Large Language Models in recognizing lexical entailment relations among verbs through differently devised prompting strategies and zero-/few-shot settings over verb pairs from two lexical databases, namely WordNet and HyperLex. Our findings unveil that the models can tackle the lexical entailment recognition task with moderately good performance, although at varying degree of effectiveness and under different conditions. Also, utilizing few-shot prompting can enhance the models' performance. However, perfectly solving the task arises as an unmet challenge for all examined LLMs, which raises an emergence for further research developments on this topic.
Making Reasoning Matter: Measuring and Improving Faithfulness of Chain-of-Thought Reasoning
Authors: Robert West, Antoine Bosselut, Boi Faltings Debjit Paul
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Large language models (LLMs) have been shown to perform better when asked to reason step-by-step before answering a question. However, it is unclear to what degree the model`s final answer is faithful to the stated reasoning steps. In this paper, we perform a causal mediation analysis on twelve LLMs to examine how intermediate reasoning steps generated by the LLM influence the final outcome and find that LLMs do not reliably use their intermediate reasoning steps when generating an answer. To address this issue, we introduce FRODO, a framework to tailor small-sized LMs to generate correct reasoning steps and robustly reason over these steps. FRODO consists of an inference module that learns to generate correct reasoning steps using an implicit causal reward function and a reasoning module that learns to faithfully reason over these intermediate inferences using a counterfactual and causal preference objective. Our experiments show that FRODO significantly outperforms four competitive baselines. Furthermore, FRODO improves the robustness and generalization ability of the reasoning LM, yielding higher performance on out-of-distribution test sets. Finally, we find that FRODO`s rationales are more faithful to its final answer predictions than standard supervised fine-tuning.
Self-training Large Language Models through Knowledge Detection
Authors: Teddy Ferdinan, Przemyslaw Kazienko, Ranjan Satapathy, Erik Cambria Yeo Wei Jie
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Large language models (LLMs) often necessitate extensive labeled datasets and training compute to achieve impressive performance across downstream tasks. This paper explores a self-training paradigm, where the LLM autonomously curates its own labels and selectively trains on unknown data samples identified through a reference-free consistency method. Empirical evaluations demonstrate significant improvements in reducing hallucination in generation across multiple subjects. Furthermore, the selective training framework mitigates catastrophic forgetting in out-of-distribution benchmarks, addressing a critical limitation in training LLMs. Our findings suggest that such an approach can substantially reduce the dependency on large labeled datasets, paving the way for more scalable and cost-effective language model training.
VE-KD: Vocabulary-Expansion Knowledge-Distillation for Training Smaller Domain-Specific Language Models
Authors: Tomohiro Yamasaki, Kazunori Imoto Pengju Gao
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: We propose VE-KD, a novel method that balances knowledge distillation and vocabulary expansion with the aim of training efficient domain-specific language models. Compared with traditional pre-training approaches, VE-KD exhibits competitive performance in downstream tasks while reducing model size and using fewer computational resources. Additionally, VE-KD refrains from overfitting in domain adaptation. Our experiments with different biomedical domain tasks demonstrate that VE-KD performs well compared with models such as BioBERT (+1% at HoC) and PubMedBERT (+1% at PubMedQA), with about 96% less training time. Furthermore, it outperforms DistilBERT and Adapt-and-Distill, showing a significant improvement in document-level tasks. Investigation of vocabulary size and tolerance, which are hyperparameters of our method, provides insights for further model optimization. The fact that VE-KD consistently maintains its advantages, even when the corpus size is small, suggests that it is a practical approach for domain-specific language tasks and is transferrable to different domains for broader applications.
Adaptive Contrastive Search: Uncertainty-Guided Decoding for Open-Ended Text Generation
Authors: Julian Rodemann, Meimingwei Li, Christian Heumann, Matthias Aßenmacher Esteban Garces Arias
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Despite the remarkable capabilities of large language models, generating high-quality text remains a challenging task. Numerous decoding strategies---such as beam search, sampling with temperature, top-$k$ sampling, nucleus (top-$p$) sampling, typical decoding, contrastive decoding, and contrastive search---have been proposed to address these challenges by improving coherence, diversity, and resemblance to human-generated text. In this study, we introduce Adaptive Contrastive Search (ACS), a novel decoding strategy that extends contrastive search (CS) by incorporating an adaptive degeneration penalty informed by the model`s estimated uncertainty at each generation step. ACS aims to enhance creativity and diversity while maintaining coherence to produce high-quality outputs. Extensive experiments across various model architectures, languages, and datasets demonstrate that our approach improves both creativity and coherence, underscoring its effectiveness in text-generation tasks. We release our code, datasets, and models to facilitate further research.
SSP: Self-Supervised Prompting for Cross-Lingual Transfer to Low-Resource Languages using Large Language Models
Authors: Aniruddha Deb, Ankish Kumar Chandresh, Parag Singla, Mausam . Vipul Kumar Rathore
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Recently, very large language models (LLMs) have shown exceptional performance on several English NLP tasks with just in-context learning (ICL), but their utility in other languages is still underexplored. We investigate their effectiveness for NLP tasks in low-resource languages (LRLs), especially in the setting of zero-labelled cross-lingual transfer (0-CLT), where no labelled training data for the target language is available -- however training data from one or more related medium-resource languages (MRLs) is utilized, alongside the available unlabeled test data for a target language. We introduce Self-Supervised Prompting (SSP), a novel ICL approach tailored for the 0-CLT setting. SSP is based on the key observation that LLMs output more accurate labels if in-context exemplars are from the target language (even if their labels are slightly noisy). To operationalize this, since target language training data is not available in 0-CLT, SSP operates in two stages. In Stage I, using source MRL training data, target language`s test data is noisily labeled. In Stage II, these noisy test data points are used as exemplars in ICL for further improved labelling. Additionally, our implementation of SSP uses a novel Integer Linear Programming (ILP)-based exemplar selection that balances similarity, prediction confidence (when available) and label coverage. Experiments on three tasks and eleven LRLs (from three regions) demonstrate that SSP strongly outperforms existing SOTA fine-tuned and prompting-based baselines in 0-CLT setup.
Re-examining Sexism and Misogyny Classification with Annotator Attitudes
Authors: Nikolas Vitsakis, Tanvi Dinkar, Gavin Abercrombie, Ioannis Konstas Aiqi Jiang
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Gender-Based Violence (GBV) is an increasing problem online, but existing datasets fail to capture the plurality of possible annotator perspectives or ensure the representation of affected groups. We revisit two important stages in the moderation pipeline for GBV: (1) manual data labelling; and (2) automated classification. For (1), we examine two datasets to investigate the relationship between annotator identities and attitudes and the responses they give to two GBV labelling tasks. To this end, we collect demographic and attitudinal information from crowd-sourced annotators using three validated surveys from Social Psychology. We find that higher Right Wing Authoritarianism scores are associated with a higher propensity to label text as sexist, while for Social Dominance Orientation and Neosexist Attitudes, higher scores are associated with a negative tendency to do so.For (2), we conduct classification experiments using Large Language Models and five prompting strategies, including infusing prompts with annotator information. We find: (i) annotator attitudes affect the ability of classifiers to predict their labels; (ii) including attitudinal information can boost performance when we use well-structured brief annotator descriptions; and (iii) models struggle to reflect the increased complexity and imbalanced classes of the new label sets.
When ”A Helpful Assistant” Is Not Really Helpful: Personas in System Prompts Do Not Improve Performances of Large Language Models
Authors: Jiaxin Pei, Lajanugen Logeswaran, Moontae Lee, David Jurgens Mingqian Zheng
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Prompting serves as the major way humans interact with Large Language Models (LLM). Commercial AI systems commonly define the role of the LLM in system prompts. For example, ChatGPT uses ”You are a helpful assistant” as part of its default system prompt. Despite current practices of adding personas to system prompts, it remains unclear how different personas affect a model`s performance on objective tasks. In this study, we present a systematic evaluation of personas in system prompts. We curate a list of 162 roles covering 6 types of interpersonal relationships and 8 domains of expertise. Through extensive analysis of 4 popular families of LLMs and 2,410 factual questions, we demonstrate that adding personas in system prompts does not improve model performance across a range of questions compared to the control setting where no persona is added. Nevertheless, further analysis suggests that the gender, type, and domain of the persona can all influence the resulting prediction accuracies. We further experimented with a list of persona search strategies and found that, while aggregating results from the best persona for each question significantly improves prediction accuracy, automatically identifying the best persona is challenging, with predictions often performing no better than random selection. Overall, our findings suggest that while adding a persona may lead to performance gains in certain settings, the effect of each persona can be largely random. %Our results can help inform the design of system prompts for AI systems. Code and data are available at https://github.com/Jiaxin-Pei/Prompting-with-Social-Roles.
Towards Efficient Visual-Language Alignment of the Q-Former for Visual Reasoning Tasks
Authors: Adam Lee, Junyoung Park, Andrew Chung, Jusang Oh, Jay-Yoon Lee Sungkyung Kim
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Recent advancements in large language models have demonstrated enhanced capabilities in visual reasoning tasks by employing additional encoders for aligning different modalities. While the Q-Former has been widely used as a general encoder for aligning several modalities including image, video, audio, and 3D with large language models, previous works on its efficient training and the analysis of its individual components have been limited. In this work, we investigate the effectiveness of parameter efficient fine-tuning (PEFT) the Q-Former using InstructBLIP with visual reasoning benchmarks ScienceQA and IconQA. We observe that applying PEFT to the Q-Former achieves comparable performance to full fine-tuning using under 2% of the trainable parameters. Additionally, we employ AdaLoRA for dynamic parameter budget reallocation to examine the relative importance of the Q-Former`s sublayers with 4 different benchmarks. Our findings reveal that the self-attention layers are noticeably more important in perceptual visual-language reasoning tasks, and relative importance of FFN layers depends on the complexity of visual-language patterns involved in tasks. The code is available at https://github.com/AttentionX/InstructBLIP_PEFT.
Modeling Gender and Dialect Bias in Automatic Speech Recognition
Authors: Chijioke Mgbahurike, Neha Kumar, Diyi Yang Camille Harris
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Dialect and gender-based biases have become an area of concern in language-dependent AI systemsincluding around automatic speech recognition (ASR) which processes speech audio into text. These potential biases raise concern for discriminatory outcomes with AI systems depending on demographic- particularly gender discrimination against women, and racial discrimination against minorities with ethnic or cultural English dialects.As such we aim to evaluate the performance of ASR systems across different genders and across dialects of English. Concretely, we take a deep dive of the performance of ASR systems on men and women across four US-based English dialects: Standard American English (SAE), African American Vernacular English (AAVE), Chicano English, and Spanglish. To do this, we construct a labeled dataset of 13 hours of podcast audio, transcribed by speakers of the represented dialects. We then evaluate zero-shot performance of different automatic speech recognition models on our dataset, and further finetune models to better understand how finetuning can impact performance. Our work fills the gap of investigating possible gender disparities within underrepresented dialects.
Are Large Language Models Consistent over Value-laden Questions?
Authors: Tanvi Deshpande, Diyi Yang Jared Moore
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Large language models (LLMs) appear to bias their survey answers toward certain values. Nonetheless, some argue that LLMs are too inconsistent to simulate particular values. Are they? To answer, we first define value consistency as the similarity of answers across 1) \textitparaphrases of one question, 2) related questions under one \textittopic, 3) multiple-choice and open-ended \textituse-cases of one question, and 4) \textitmultilingual translations of a question to English, Chinese, German, and Japanese. We apply these measures to a few large, open LLMs including llama-3, as well as gpt-4o, using eight thousand questions spanning more than 300 topics. Unlike prior work, we find that \textitmodels are relatively consistent across paraphrases, use-cases, translations, and within a topic. Still, some inconsistencies remain. Models are more consistent on uncontroversial topics (e.g., in the U.S., “Thanksgiving”) than on controversial ones (e.g. “euthanasia”). Base models are both more consistent compared to fine-tuned models and are uniform in their consistency across topics, while fine-tuned models are more inconsistent about some topics (e.g. “euthanasia”) than others (e.g. “Women`s rights”) like our human participants.
xTower: A Multilingual LLM for Explaining and Correcting Translation Errors
Authors: Nuno M Guerreiro, Sweta Agrawal, Ricardo Rei, José Pombal, Tania Vaz, Helena Wu, Beatriz Silva, Daan Van Stigt, Andre Martins Marcos V Treviso
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: While machine translation (MT) systems are achieving increasingly strong performance on benchmarks, they often produce translations with errors and anomalies. Understanding these errors can potentially help improve the translation quality and user experience. This paper introduces xTower, an open large language model (LLM) built on top of TowerBase designed to provide free-text explanations for translation errors in order to guide the generation of a corrected translation. The quality of the generated explanations by xTower are assessed via both intrinsic and extrinsic evaluation. We ask expert translators to evaluate the quality of the explanations across two dimensions: relatedness towards the error span being explained and helpfulness in error understanding and improving translation quality. Extrinsically, we test xTower across various experimental setups in generating translation corrections, demonstrating significant improvements in translation quality. Our findings highlight xTower`s potential towards not only producing plausible and helpful explanations of automatic translations, but also leveraging them to suggest corrected translations.
LAMBDA: Large Language Model-Based Data Augmentation for Multi-Modal Machine Translation
Authors: Dongyuan Li, Jialun Shen, Yicheng Xu, Mingkun Xu, Kotaro Funakoshi, Manabu Okumura Yusong Wang
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Multi-modal machine translation (MMT) can reduce ambiguity and semantic distortion compared with traditional machine translation (MT) by utilizing auxiliary information such as images. However, current MMT methods face two primary challenges. The first is their underperformance compared to MT methods based on pre-trained models. The second is the inadequate exploitation and integration of the image modality within the model, primarily due to a lack of triplet training data. A mainstream approach is to introduce large amounts of parallel and monolingual data to train the text model and the visual model separately. However, incorporating extensive external data can result in data imbalance, which may introduce biases during training. Additionally, the collection and cleaning of such large datasets is labor-intensive. To overcome these challenges, we introduce a novel, low-cost, large language model-based data augmentation method called LAMBDA, which can enrich the original samples and expand the dataset without requiring external images and text. We propose a fine-grained image captioning module with a noise filter to hierarchically and accurately extract unexploited information from images. Additionally, we design two specific prompts to guide the GPT-3.5 model in generating enriched texts and the corresponding translations. The enriched samples contain diverse text and strong connections between text and images, leading to significant improvements for MMT baselines, with the highest being an increase of up to 3.83 BLEU score and 3.61 METEOR score.
Evaluating Differentially Private Synthetic Data Generation in High-Stakes Domains
Authors: Nupoor Gandhi, Pulkit Madaan, Lisa Bauer, Charith Peris, Anjalie Field Krithika Ramesh
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: The difficulty of anonymizing text data hinders the development and deployment of NLP in high-stakes domains that involve private data, such as healthcare and social services. Poorly anonymized sensitive data cannot be easily shared with annotators or external researchers, nor can it be used to train public models. In this work, we explore the feasibility of using synthetic data generated from differentially private language models in place of real data to facilitate the development of NLP in these domains without compromising privacy. In contrast to prior work, we generate synthetic data for real high-stakes domains, and we propose and conduct use-inspired evaluations to assess data quality. Our results show that prior simplistic evaluations have failed to highlight utility, privacy, and fairness issues in the synthetic data. Overall, our work underscores the need for further improvements to synthetic data generation for it to be a viable way to enable privacy-preserving data sharing.
Dual Process Masking for Dialogue Act Recognition
Authors: Halim Acosta, Wookhee Min, Jonathan Rowe, Bradford Mott, Snigdha Chaturvedi, James Lester Yeo Jin Kim
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Dialogue act recognition is the task of classifying conversational utterances based on their communicative intent or function. To address this problem, we propose a novel two-phase processing approach called Dual-Process Masking. This approach streamlines the task by masking less important tokens in the input, identified through retrospective analysis of their estimated contribution during training. It enhances interpretability by using the masks applied during classification learning. Dual-Process Masking significantly improves performance over strong baselines for dialogue act recognition on a collaborative problem-solving dataset and three public dialogue benchmarks.
XC-Cache: Cross-Attending to Cached Context for Efficient LLM Inference
Authors: Étienne Marcotte, Pierre-Andre Noel, Valentina Zantedeschi, David Vazquez, Nicolas Chapados, Christopher Pal, Perouz Taslakian Joao Monteiro
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Prompts are often employed to condition decoder-only language model generation on reference information. Just-in-time processing of a context is inefficient due to the quadratic cost of self-attention operations, and caching is desirable. However, caching transformer states can easily require almost as much space as the model parameters. When the right context is not known in advance, caching the prompt can be challenging. This work addresses these limitations by introducing models that, inspired by the encoder-decoder architecture, use cross-attention to condition generation on reference text without the prompt. More precisely, we leverage pre-trained decoder-only models and only train a small number of added layers. We use Question-Answering (QA) as a testbed to evaluate the ability of our models to perform conditional generation and observe that they outperform prompt-based inference methods, are comparable to fine-tuned prompted LLMs, and drastically reduce the space footprint relative to standard KV caching by two orders of magnitude. Specifically, we introduced XC-Llama which converts a pre-trained Llama 2 into an encoder-decoder architecture by integrating cross-attention layers interleaved in between existing self-attention layers.
Pioneering Reliable Assessment in Text-to-Image Knowledge Editing: Leveraging a Fine-Grained Dataset and an Innovative Criterion
Authors: Kaixiong Zhou, Yili Wang, Ruobing Wang, Xin Wang Hengrui Gu
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: During pre-training, the Text-to-Image (T2I) diffusion models encode factual knowledge into their parameters. These parameterized facts enable realistic image generation, but they may become obsolete over time, thereby misrepresenting the current state of the world. Knowledge editing techniques aim to update model knowledge in a targeted way. However, facing the dual challenges posed by inadequate editing datasets and unreliable evaluation criterion, the development of T2I knowledge editing encounter difficulties in effectively generalizing injected knowledge. In this work, we design a T2I knowledge editing framework by comprehensively spanning on three phases: First, we curate a dataset \textbfCAKE, comprising paraphrase and multi-object test, to enable more fine-grained assessment on knowledge generalization. Second, we propose a novel criterion, \textbfadaptive CLIP threshold, to effectively filter out false successful images under the current criterion and achieve reliable editing evaluation. Finally, we introduce \textbfMPE, a simple but effective approach for T2I knowledge editing. Instead of tuning parameters, MPE precisely recognizes and edits the outdated part of the conditioning text-prompt to accommodate the up-to-date knowledge. A straightforward implementation of MPE (Based on in-context learning) exhibits better overall performance than previous model editors. We hope these efforts can further promote faithful evaluation of T2I knowledge editing methods.
Eigen Attention: Attention in Low-Rank Space for KV Cache Compression
Authors: Gobinda Saha, Sakshi Choudhary, Kaushik Roy Utkarsh Saxena
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Large language models (LLMs) represent a groundbreaking advancement in the domain of natural language processing due to their impressive reasoning abilities. Recently, there has been considerable interest in increasing the context lengths for these models to enhance their applicability to complex tasks. However, at long context lengths and large batch sizes, the key-value (KV) cache, which stores the attention keys and values, emerges as the new bottleneck in memory usage during inference. To address this, we propose Eigen Attention, which performs the attention operation in a low-rank space, thereby reducing the KV cache memory overhead. Our proposed approach is orthogonal to existing KV cache compression techniques and can be used synergistically with them. Through extensive experiments over OPT, MPT, and Llama model families, we demonstrate that Eigen Attention results in up to 40% reduction in KV cache sizes and up to 60% reduction in attention operation latency with minimal drop in performance.
ACCEPT: Adaptive Codebook for Composite and Efficient Prompt Tuning
Authors: Wei-Hua Li, Jun-cheng Chen, Chu-Song Chen Yu-Chen Lin
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Prompt Tuning has been a popular Parameter-Efficient Fine-Tuning method attributed to its remarkable performance with few updated parameters on various large-scale pretrained Language Models (PLMs). Traditionally, each prompt has been considered indivisible and updated independently, leading the parameters increase proportionally as prompt length grows. To address this issue, we propose Adaptive Codebook for Composite and Efficient Prompt Tuning (ACCEPT). In our method, we refer to the concept of product quantization (PQ), allowing all soft prompts to share a set of learnable codebook vectors in each subspace, with each prompt differentiated by a set of adaptive weights. We achieve the superior performance on 17 diverse natural language tasks including natural language understanding (NLU) and question answering (QA) tasks by tuning only 0.3% of parameters of the PLMs. Our approach also excels in few-shot and large model settings, highlighting its significant potential.
Beyond Perplexity: Multi-dimensional Safety Evaluation of LLM Compression
Authors: Ashim Gupta, Tao Li, Oliver Bentham, Vivek Srikumar Zhichao Xu
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Increasingly, model compression techniques enable large language models (LLMs) to be deployed in real-world applications. As a result of this momentum towards local deployment, compressed LLMs will interact with a large population. Prior work on compression typically prioritize preserving perplexity, which is directly analogous to training loss. The impact of compression method on other critical aspects of model behavior---particularly safety---requires systematic assessment. To this end, we investigate the impact of model compression along four dimensions: (1) degeneration harm, i.e., bias and toxicity in generation; (2) representational harm, i.e., biases in discriminative tasks; (3) dialect bias; and (4) language modeling and downstream task performance. We examine a wide spectrum of LLM compression techniques, including unstructured pruning, semi-structured pruning, and quantization. Our analysis reveals that compression can lead to unexpected consequences. Although compression may unintentionally alleviate LLMs' degeneration harm, it can still exacerbate representational harm. Furthermore, increasing compression produces a divergent impact on different protected groups. Finally, different compression methods have drastically different safety impacts: for example, quantization mostly preserves bias while pruning degrades quickly. Our findings underscore the importance of integrating safety assessments into the development of compressed LLMs to ensure their reliability across real-world applications.
One-to-many testing for code generation from (just) natural language
Authors: Mukul Singh, Gust Verbruggen, Sumit Gulwani, Vu Le Mansi Uniyal
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: MBPP is a popular dataset for evaluating the task of code generation from natural language. Despite its popularity, there are three problems: (1) it relies on providing test cases to generate the right signature, (2) there is poor alignment between instruction and evaluation test cases, and (3) contamination of the exact phrasing being present in training datasets. We adapt MBPP to emphasize on generating code from just natural language by (1) removing ambiguity about the semantics of the task from the descriptions, and (2) evaluating generated code on multiple sets of assertions to account for ambiguity in the syntax. We compare popular open and closed weight models on the original (MBPP) and adapted (MBUPP) datasets.
A Unified Framework for Model Editing
Authors: Dev Sajnani, Gopala Anumanchipalli Akshat Gupta
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: ROME and MEMIT are largely believed to be two different model editing algorithms, with the major difference between them being the ability to perform batched edits. In this paper, we unify these two algorithms under a single conceptual umbrella, optimizing for the same goal, which we call the preservation-memorization objective. ROME uses an equality constraint to optimize this objective to perform one edit at a time, whereas MEMIT employs a more flexible least-square constraint that allows for batched edits. We generalize ROME and enable batched editing with equality constraint in the form of EMMET - an Equality-constrained Mass Model Editing algorithm for Transformers, a new batched memory-editing algorithm. EMMET can perform batched-edits up to a batch-size of 10,000, with very similar performance to MEMIT across multiple dimensions. With the introduction of EMMET, we truly unify ROME and MEMIT and show that both algorithms are equivalent in terms of their optimization objective, their abilities (singular and batched editing), their model editing performance and their limitations.
M3SciQA: A Multi-Modal Multi-Document Scientific QA Benchmark for Evaluating Foundation Models
Authors: Ziyao Shangguan, Yilun Zhao, Deyuan Li, Yixin Liu, Arman Cohan Chuhan Li
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Existing evaluation benchmarks for foundation models in understanding scientific literature predominantly focus on single-document, text-only tasks. Such benchmarks often do not adequately represent the complexity of research workflows, which typically also involve interpreting non-textual data, such as figures and tables, and gathering information across multiple documents and related literature. To address this gap, we introduce M3SciQA, a multi-modal, multi-document scientific question answering benchmark designed for a more comprehensive evaluation of foundation models. M3Sci QA consists of 1452 expert-annotated questions spanning 70 natural language processing paper clusters, where each cluster represents a primary paper along with all its cited documents, mirroring the workflow of comprehending a single paper by requiring multi-modal and multi-document data. With M3SciQA, we conduct a comprehensive evaluation of 18 frontier foundation models. Our results indicate that current foundation models still significantly underperform compared to human experts in multi-modal information retrieval and in reasoning across multiple scientific documents. Additionally, we explore the implications of these findings for the future advancement of applying foundation models in multi-modal scientific literature analysis.
Probing the Capacity of Language Model Agents to Operationalize Disparate Experiential Context Despite Distraction
Authors: Chris Sypherd, Dylan Cashman Sonny George
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Large language model (LLM) agents show promise in an increasing number of domains. In many proposed applications, it is expected that the agent reasons over accumulated experience presented in an input prompt. We propose the OEDD (Operationalize Experience Despite Distraction) corpus, a human-annotator-validated body of scenarios with pre-scripted agent histories where the agent must make a decision based on disparate experiential information in the presence of a distractor. We evaluate three state-of-the-art LLMs (GPT-3.5 Turbo, GPT-4o, and Gemini 1.5 Pro) using a minimal chain-of-thought prompting strategy and observe that when (1) the input context contains over 1,615 tokens of historical interactions, (2) a crucially decision-informing premise is the rightful conclusion over two disparate environment premises, and (3) a trivial, but distracting red herring fact follows, all LLMs perform worse than random choice at selecting the better of two actions. Our code and test corpus are publicly available at: [github.com/sonnygeorge/OEDD](github.com/sonnygeorge/OEDD).
Knowledge-Centric Templatic Views of Documents
Authors: Silviu Cucerzan, Allen Herring, Vuksan Mijovic, Erik Oveson, Sujay Kumar Jauhar Isabel Alyssa Cachola
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Authors seeking to communicate with broader audiences often share their ideas in various document formats, such as slide decks, newsletters, reports, and posters. Prior work on document generation has generally tackled the creation of each separate format to be a different task, leading to fragmented learning processes, redundancy in models and methods, and disjointed evaluation. We consider each of these documents as templatic views of the same underlying knowledge/content, and we aim to unify the generation and evaluation of these templatic views. We begin by showing that current LLMs are capable of generating various document formats with little to no supervision. Further, a simple augmentation involving a structured intermediate representation can improve performance, especially for smaller models. We then introduce a novel unified evaluation framework that can be adapted to measuring the quality of document generators for heterogeneous downstream applications. This evaluation is adaptable to a range of user defined criteria and application scenarios, obviating the need for task specific evaluation metrics. Finally, we conduct a human evaluation, which shows that people prefer 82% of the documents generated with our method, while correlating more highly with our unified evaluation framework than prior metrics in the literature.
Shoes-ACOSI: A Dataset for Aspect-Based Sentiment Analysis with Implicit Opinion Extraction
Authors: Wenzhao Qiu, Ryan Bruggeman, Yi Han, Estefania Ciliotta Chehade, Lu Wang Joseph J Peper
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: We explore *implicit opinion extraction* as a new component of aspect-based sentiment analysis (ABSA) systems. Prior work in ABSA has investigated opinion extraction as an important subtask, however, these works only label concise, *explicitly*-stated opinion spans. In this work, we present **Shoes-ACOSI**, a new and challenging ABSA dataset in the e-commerce domain with implicit opinion span annotations, the first of its kind. Shoes-ACOSI builds upon the existing Aspect-Category-Opinion-Sentiment (ACOS) quadruple extraction task, extending the task to quintuple extraction---now localizing and differentiating both implicit and explicit opinion. In addition to the new annotation schema, our dataset contains paragraph-length inputs which, importantly, present complex challenges through increased input length, increased number of sentiment expressions, and more mixed-sentiment-polarity examples when compared with existing benchmarks. We quantify the difficulty of our new dataset by evaluating with state-of-the-art fully-supervised and prompted-LLM baselines. We find our dataset presents significant challenges for both supervised models and LLMs, particularly from the new implicit opinion extraction component of the ACOSI task, highlighting the need for continued research into implicit opinion understanding.
Socratic Human Feedback (SoHF): Expert Steering Strategies for LLM Code Generation
Authors: Li Erran Li, Min Bai, Xiaopeng Li, Kaixiang Lin, Xiong Zhou, Alex C. Williams Subramanian Chidambaram
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Large Language Models (LLMs) are increasingly used for generating code solutions, empowered by features like self-debugging and self-reflection. However, LLMs often struggle with complex programming problems without human guidance. This paper investigates the strategies employed by expert programmers to steer code-generating LLMs toward successful outcomes. Through a study involving experts using natural language to guide GPT-4, Gemini Ultra, and, Claude 3.5 Sonnet on highly difficult programming challenges, we frame our analysis using the “Socratic Feedback” paradigm for understanding effective steering strategies. By analyzing 30 conversational transcripts across all three models, we map observed feedback strategies to five stages of Socratic Questioning: Definition, Elenhus, Maieutic, Dialectic, and Counter-factual reasoning. We find evidence that by employing a combination of different Socratic feedback strategies across multiple turns, programmers successfully guided the models to solve 74% of the problems that the models initially failed to solve on their own.
Large Language Models Know What To Say But Not When To Speak
Authors: Vasanth Sarathy, Jan Ruiter Muhammad Umair
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Turn-taking is a fundamental mechanism in human communication that ensures smooth and coherent verbal interactions. Recent advances in Large Language Models (LLMs) have motivated their use in improving the turn-taking capabilities of Spoken Dialogue Systems (SDS), such as their ability to respond at appropriate times. However, existing models often struggle to predict \textitopportunities for speaking --- called Transition Relevance Places (TRPs) --- in natural, unscripted conversations, focusing only on turn-final TRPs and not within-turn TRPs. To address these limitations, we introduce a novel dataset of participant-labeled within-turn TRPs and use it to evaluate the performance of state-of-the-art LLMs in predicting opportunities for speaking. Our experiments reveal the current limitations of LLMs in modeling unscripted spoken interactions, highlighting areas for improvement and paving the way for more naturalistic dialogue systems.
Towards Explainable Chinese Native Learner Essay Fluency Assessment: Dataset, Tasks, and Method
Authors: Hongyi Wu, Yadong Zhang, Man Lan, Xiaopeng Bai, Shaoguang Mao, Yuanbin Wu, Xinlin Zhuang, Li Cai Xinshu Shen
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Grammatical Error Correction (GEC) is a crucial technique in Automated Essay Scoring (AES) for evaluating the fluency of essays. However, in Chinese, existing GEC datasets often fail to consider the importance of specific grammatical error types within compositional scenarios, lack research on data collected from native Chinese speakers, and largely overlook cross-sentence grammatical errors. Furthermore, the measurement of the overall fluency of an essay is often overlooked. To address these issues, we present CEFA (Chinese Essay Fluency Assessment), an extensive corpus that is derived from essays authored by native Chinese-speaking primary and secondary students and encapsulates essay fluency scores along with both coarse and fine-grained grammatical error types and corrections. Experiments employing various benchmark models on CEFA substantiate the challenge of our dataset. Our findings further highlight the significance of fine-grained annotations in fluency assessment and the mutually beneficial relationship between error types and corrections
CoCoHD: Congress Committee Hearing Dataset
Authors: Yunsong Liu, Mingxiao Song, Agam Shah, Sudheer Chava Arnav Hiray
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: U.S. congressional hearings significantly influence the national economy and social fabric, impacting individual lives. Despite their importance, there is a lack of comprehensive datasets for analyzing these discourses. To address this, we propose the **Co**ngress **Co**mmittee **H**earing **D**ataset (CoCoHD), covering hearings from 1997 to 2024 across 86 committees, with 32,697 records. This dataset enables researchers to study policy language on critical issues like healthcare, LGBTQ+ rights, and climate justice. We demonstrate its potential with a case study on 1,000 energy-related sentences, analyzing the Energy and Commerce Committee`s stance on fossil fuel consumption. By fine-tuning pre-trained language models, we create energy-relevant measures for each hearing. Our market analysis shows that natural language analysis using CoCoHD can predict and highlight trends in the energy sector.
Student Data Paradox and Curious Case of Single Student-Tutor Model: Regressive Side Effects of Training LLMs for Personalized Learning
Authors: Naiming Liu, Richard Baraniuk Shashank Sonkar
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: The pursuit of personalized education has led to the integration of Large Language Models (LLMs) in developing intelligent tutoring systems. To better understand and adapt to individual student needs, including their misconceptions, LLMs need to be trained on extensive datasets of student-tutor dialogues. Our research uncovers a fundamental challenge in this approach: the “Student Data Paradox”. This paradox emerges when LLMs, trained on student data to understand learner behavior, inadvertently compromise their own factual knowledge and reasoning abilities. We investigate this paradox by training state-of-the-art language models on student-tutor dialogue datasets and evaluating their performance across multiple benchmarks. These benchmarks assess various aspects of language model capabilities, including reasoning, truthfulness, and common sense understanding. Our findings reveal significant declines in the models' performance across these diverse benchmarks, indicating a broad impact on their capabilities when trained to model student behavior. Our research makes two primary contributions: (1) empirical demonstration of the Student Data Paradox through quantitative analysis of model performance, and (2) introduction of “hallucination tokens” as a mitigation strategy. These tokens, while improving performance, highlight the persistent challenge of balancing accurate student behavior modeling with maintaining the LLM`s integrity as an educational tool.This study emphasizes the need for innovative solutions to reconcile the conflicting goals of faithfully understanding diverse student cognition while preserving the model`s ability to provide accurate information and guidance.
MalAlgoQA: Pedagogical Evaluation of Counterfactual Reasoning in Large Language Models and Implications for AI in Education
Authors: Naiming Liu, MyCo Le, Richard Baraniuk Shashank Sonkar
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: This paper introduces MalAlgoQA, a novel dataset designed to evaluate the counterfactual reasoning capabilities of Large Language Models (LLMs) through a pedagogical approach. The dataset comprises mathematics and reading comprehension questions, each accompanied by four answer choices and their corresponding rationales. At the heart of MalAlgoQA are “malgorithms” - rationales behind incorrect answer choices that represent flawed yet logically coherent reasoning paths. These malgorithms serve as counterfactual scenarios, allowing us to assess an LLM`s ability to identify and analyze flawed reasoning patterns. We propose the Malgorithm Identification task, where LLMs are assessed based on their ability to identify corresponding malgorithm given an incorrect answer choice. To evaluate the model performance, we introduce two metrics: Algorithm Identification Accuracy (AIA) for correct answer rationale identification, and Malgorithm Identification Accuracy (MIA) for incorrect answer rationale identification. Our experiments reveal that state-of-the-art LLMs exhibit significant performance drops in MIA compared to AIA, highlighting the challenges in counterfactual reasoning.Surprisingly, we find that the chain-of-thought prompting technique not only fails to consistently enhance MIA but can sometimes lead to underperformance compared to simple prompting. These findings have important implications for developing LLMs with improved counterfactual reasoning, particularly relevant for AI-powered tutoring systems, where identifying and addressing student misconceptions is essential. MalAlgoQA dataset is available here.
Sonnet or Not, Bot? Poetry Evaluation for Large Models and Datasets
Authors: Anna Preus, Maria Antoniak Melanie Walsh
Published in: Findings of the Association for Computational Linguistics: EMNLP 2024 (Findings Track), 2024

Abstract: Large language models (LLMs) can now generate and recognize poetry. But what do LLMs really know about poetry? We develop a task to evaluate how well LLMs recognize one aspect of English-language poetry---poetic form---which captures many different poetic features, including rhyme scheme, meter, and word or line repetition. By using a benchmark dataset of over 4.1k human expert-annotated poems, we show that state-of-the-art LLMs can successfully identify both common and uncommon fixed poetic forms---such as sonnets, sestinas, and pantoums---with surprisingly high accuracy. However, performance varies significantly by poetic form; the models struggle to identify unfixed poetic forms, especially those based on topic or visual features. We additionally measure how many poems from our benchmark dataset are present in popular pretraining datasets or memorized by GPT-4, finding that pretraining presence and memorization may improve performance on this task, but results are inconclusive. We release a benchmark evaluation dataset with 1.4k public domain poems and form annotations, results of memorization experiments and data audits, and code.