pwc-archive/papers-with-abstracts huggingface dataset:
Logic Haystacks: Probing LLMs Long-Context Logical Reasoning (Without Easily Identifiable Unrelated Padding)
Authors: Damien Sileo
Date: 2025-02-24 00:00:00
Tasks: Logical Reasoning, Retrieval

Large language models demonstrate promising long context processing capabilities, with recent models touting context windows close to one million tokens. However, the evaluations supporting these claims often involve simple retrieval tasks or synthetic tasks padded with irrelevant text, which the models may easily detect and discard. In this work, we generate lengthy simplified English text with first-order logic representations spanning up to 2048 clauses (around 25k GPT-4 tokens). We formulate an evaluation task with evidence retrieval for contradiction detection. The long, homogeneous text is filled with distractors that are both hard to distinguish from relevant evidences and provably not interfering with them. Our evaluation of evidence retrieval shows that the effective context window is much smaller with realistic distractors, already crumbling at 128 clauses.
Measuring Data Diversity for Instruction Tuning: A Systematic Analysis and A Reliable Metric
Authors: Yuming Yang, Yang Nan, Junjie Ye, Shihan Dou, Xiao Wang, Shuo Li, Huijie Lv, Tao Gui, Qi Zhang, Xuanjing Huang
Date: 2025-02-24 00:00:00
Tasks: Diversity

Data diversity is crucial for the instruction tuning of large language models. Existing studies have explored various diversity-aware data selection methods to construct high-quality datasets and enhance model performance. However, the fundamental problem of precisely defining and measuring data diversity remains underexplored, limiting clear guidance for data engineering. To address this, we systematically analyze 11 existing diversity measurement methods by assessing their correlation with model performance through extensive fine-tuning experiments. Our results indicate that a reliable diversity measure should properly account for both inter-sample differences and the information distribution in the sample space. Building on this, we propose NovelSum, a new diversity metric based on sample-level "novelty." Experiments on both simulated and real-world data show that NovelSum accurately captures diversity variations and achieves a 0.97 correlation with instruction-tuned model performance, highlighting its value in guiding data engineering practices. With NovelSum as an optimization objective, we further develop a greedy, diversity-oriented data selection strategy that outperforms existing approaches, validating both the effectiveness and practical significance of our metric.
Baichuan-Audio: A Unified Framework for End-to-End Speech Interaction
Authors: Tianpeng Li, Jun Liu, Tao Zhang, Yuanbo Fang, Da Pan, Mingrui Wang, Zheng Liang, zehuan li, MingAn Lin, Guosheng Dong, Jianhua Xu, Haoze Sun, Zenan Zhou, WeiPeng Chen
Date: 2025-02-24 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, Question Answering

We introduce Baichuan-Audio, an end-to-end audio large language model that seamlessly integrates audio understanding and generation. It features a text-guided aligned speech generation mechanism, enabling real-time speech interaction with both comprehension and generation capabilities. Baichuan-Audio leverages a pre-trained ASR model, followed by multi-codebook discretization of speech at a frame rate of 12.5 Hz. This multi-codebook setup ensures that speech tokens retain both semantic and acoustic information. To further enhance modeling, an independent audio head is employed to process audio tokens, effectively capturing their unique characteristics. To mitigate the loss of intelligence during pre-training and preserve the original capabilities of the LLM, we propose a two-stage pre-training strategy that maintains language understanding while enhancing audio modeling. Following alignment, the model excels in real-time speech-based conversation and exhibits outstanding question-answering capabilities, demonstrating its versatility and efficiency. The proposed model demonstrates superior performance in real-time spoken dialogue and exhibits strong question-answering abilities. Our code, model and training data are available at https://github.com/baichuan-inc/Baichuan-Audio
MULTITAT: Benchmarking Multilingual Table-and-Text Question Answering
Authors: Xuanliang Zhang, Dingzirui Wang, Keyan Xu, Qingfu Zhu, Wanxiang Che
Date: 2025-02-24 00:00:00
Tasks: Benchmarking, Question Answering

Question answering on the hybrid context of tables and text (TATQA) is a critical task, with broad applications in data-intensive domains. However, existing TATQA datasets are limited to English, leading to several drawbacks: (i) They overlook the challenges of multilingual TAT-QA and cannot assess model performance in the multilingual setting. (ii) They do not reflect real-world scenarios where tables and texts frequently appear in non-English languages. To address the limitations, we propose the first multilingual TATQA dataset (MULTITAT). Specifically, we sample data from 3 mainstream TATQA datasets and translate it into 10 diverse languages. To align the model TATQA capabilities in English with other languages, we develop a baseline, Ours. Experimental results reveal that the performance on non-English data in MULTITAT drops by an average of 19.4% compared to English, proving the necessity of MULTITAT. We further analyze the reasons for this performance gap. Furthermore, Ours outperforms other baselines by an average of 3.3, demonstrating its effectiveness.
MonoTODia: Translating Monologue Requests to Task-Oriented Dialogues
Authors: Sebastian Steindl, Ulrich Schäfer, Bernd Ludwig
Date: 2025-02-24 00:00:00

Data scarcity is one of the main problems when it comes to real-world applications of transformer-based models. This is especially evident for task-oriented dialogue (TOD) systems, which require specialized datasets, that are usually not readily available. This can hinder companies from adding TOD systems to their services. This study therefore investigates a novel approach to sourcing annotated dialogues from existing German monologue material. Focusing on a real-world example, we investigate whether these monologues can be transformed into dialogue formats suitable for training TOD systems. We show the approach with the concrete example of a company specializing in travel bookings via e-mail. We fine-tune state-of-the-art Large Language Models for the task of rewriting e-mails as dialogues and annotating them. To ensure the quality and validity of the generated data, we employ crowd workers to evaluate the dialogues across multiple criteria and to provide gold-standard annotations for the test dataset. We further evaluate the usefulness of the dialogues for training TOD systems. Our evaluation shows that the dialogues and annotations are of high quality and can serve as a valuable starting point for training TOD systems. Finally, we make the annotated dataset publicly available to foster future research.
Extracting domain-specific terms using contextual word embeddings
Authors: Andraž Repar, Nada Lavrač, Senja Pollak
Date: 2025-02-24 00:00:00
Tasks: Term Extraction, Word Embeddings

Automated terminology extraction refers to the task of extracting meaningful terms from domain-specific texts. This paper proposes a novel machine learning approach to terminology extraction, which combines features from traditional term extraction systems with novel contextual features derived from contextual word embeddings. Instead of using a predefined list of part-of-speech patterns, we first analyse a new term-annotated corpus RSDO5 for the Slovenian language and devise a set of rules for term candidate selection and then generate statistical, linguistic and context-based features. We use a support-vector machine algorithm to train a classification model, evaluate it on the four domains (biomechanics, linguistics, chemistry, veterinary) of the RSDO5 corpus and compare the results with state-of-art term extraction approaches for the Slovenian language. Our approach provides significant improvements in terms of F1 score over the previous state-of-the-art, which proves that contextual word embeddings are valuable for improving term extraction.
Improving the Inclusivity of Dutch Speech Recognition by Fine-tuning Whisper on the JASMIN-CGN Corpus
Authors: Golshid Shekoufandeh, Paul Boersma, Antal Van den Bosch
Date: 2025-02-24 00:00:00
Tasks: Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

We test and study the variation in speech recognition of fine-tuned versions of the Whisper model on child, elderly and non-native Dutch speech from the JASMIN-CGN corpus. Our primary goal is to evaluate how speakers' age and linguistic background influence Whisper's performance. Whisper achieves varying Word Error Rates (WER) when fine-tuned on subpopulations of specific ages and linguistic backgrounds. Fine-tuned performance is remarkably better than zero-shot performance, achieving a relative reduction in WER of 81% for native children, 72% for non-native children, 67% for non-native adults, and 65% for native elderly people. Our findings underscore the importance of training speech recognition models like Whisper on underrepresented subpopulations such as children, the elderly, and non-native speakers.
`Generalization is hallucination' through the lens of tensor completions
Authors: Liang Ze Wong
Date: 2025-02-24 00:00:00
Tasks: Hallucination, Position

In this short position paper, we introduce tensor completions and artifacts and make the case that they are a useful theoretical framework for understanding certain types of hallucinations and generalizations in language models.
Implicit Word Reordering with Knowledge Distillation for Cross-Lingual Dependency Parsing
Authors: Zhuoran Li, Chunming Hu, Junfan Chen, Zhijun Chen, Richong Zhang
Date: 2025-02-24 00:00:00
Tasks: Cross-Lingual Transfer, Dependency Parsing, Knowledge Distillation, Sentence

Word order difference between source and target languages is a major obstacle to cross-lingual transfer, especially in the dependency parsing task. Current works are mostly based on order-agnostic models or word reordering to mitigate this problem. However, such methods either do not leverage grammatical information naturally contained in word order or are computationally expensive as the permutation space grows exponentially with the sentence length. Moreover, the reordered source sentence with an unnatural word order may be a form of noising that harms the model learning. To this end, we propose an Implicit Word Reordering framework with Knowledge Distillation (IWR-KD). This framework is inspired by that deep networks are good at learning feature linearization corresponding to meaningful data transformation, e.g. word reordering. To realize this idea, we introduce a knowledge distillation framework composed of a word-reordering teacher model and a dependency parsing student model. We verify our proposed method on Universal Dependency Treebanks across 31 different languages and show it outperforms a series of competitors, together with experimental analysis to illustrate how our method works towards training a robust parser.
HIPPO: Enhancing the Table Understanding Capability of Large Language Models through Hybrid-Modal Preference Optimization
Authors: Zhenghao Liu, Haolan Wang, Xinze Li, Qiushi Xiong, Xiaocui Yang, Yu Gu, Yukun Yan, Qi Shi, Fangfang Li, Ge Yu, Maosong Sun
Date: 2025-02-24 00:00:00
Tasks: Diversity, Fact Verification, Question Answering

Tabular data contains rich structural semantics and plays a crucial role in organizing and manipulating information. To better capture these structural semantics, this paper introduces the HybrId-modal Preference oPtimizatiOn (HIPPO) model, which represents tables using both text and image, and optimizes MLLMs to effectively learn more comprehensive table information from these multiple modalities. Specifically, HIPPO samples model responses from hybrid-modal table representations and designs a modality-consistent sampling strategy to enhance response diversity and mitigate modality bias during DPO training. Experimental results on table question answering and table fact verification tasks demonstrate the effectiveness of HIPPO, achieving a 4% improvement over various table reasoning models. Further analysis reveals that HIPPO not only enhances reasoning abilities based on unimodal table representations but also facilitates the extraction of crucial and distinct semantics from different modal representations. All data and codes are available at https://github.com/NEUIR/HIPPO.
Turning Conversations into Workflows: A Framework to Extract and Evaluate Dialog Workflows for Service AI Agents
Authors: Prafulla Kumar Choubey, Xiangyu Peng, Shilpa Bhagavath, Caiming Xiong, Shiva Kumar Pentyala, Chien-Sheng Wu
Date: 2025-02-24 00:00:00

Automated service agents require well-structured workflows to provide consistent and accurate responses to customer queries. However, these workflows are often undocumented, and their automatic extraction from conversations remains unexplored. In this work, we present a novel framework for extracting and evaluating dialog workflows from historical interactions. Our extraction process consists of two key stages: (1) a retrieval step to select relevant conversations based on key procedural elements, and (2) a structured workflow generation process using a question-answer-based chain-of-thought (QA-CoT) prompting. To comprehensively assess the quality of extracted workflows, we introduce an automated agent and customer bots simulation framework that measures their effectiveness in resolving customer issues. Extensive experiments on the ABCD and SynthABCD datasets demonstrate that our QA-CoT technique improves workflow extraction by 12.16\% in average macro accuracy over the baseline. Moreover, our evaluation method closely aligns with human assessments, providing a reliable and scalable framework for future research.
On Relation-Specific Neurons in Large Language Models
Authors: Yihong Liu, Runsheng Chen, Lea Hirlimann, Ahmad Dawar Hakimi, Mingyang Wang, Amir Hossein Kargaran, Sascha Rothe, François Yvon, Hinrich Schütze
Date: 2025-02-24 00:00:00
Tasks: Relation

In large language models (LLMs), certain neurons can store distinct pieces of knowledge learned during pretraining. While knowledge typically appears as a combination of relations and entities, it remains unclear whether some neurons focus on a relation itself -- independent of any entity. We hypothesize such neurons detect a relation in the input text and guide generation involving such a relation. To investigate this, we study the Llama-2 family on a chosen set of relations with a statistics-based method. Our experiments demonstrate the existence of relation-specific neurons. We measure the effect of selectively deactivating candidate neurons specific to relation $r$ on the LLM's ability to handle (1) facts whose relation is $r$ and (2) facts whose relation is a different relation $r' \neq r$. With respect to their capacity for encoding relation information, we give evidence for the following three properties of relation-specific neurons. $\textbf{(i) Neuron cumulativity.}$ The neurons for $r$ present a cumulative effect so that deactivating a larger portion of them results in the degradation of more facts in $r$. $\textbf{(ii) Neuron versatility.}$ Neurons can be shared across multiple closely related as well as less related relations. Some relation neurons transfer across languages. $\textbf{(iii) Neuron interference.}$ Deactivating neurons specific to one relation can improve LLM generation performance for facts of other relations. We will make our code publicly available at https://github.com/cisnlp/relation-specific-neurons.
What is a Good Question? Utility Estimation with LLM-based Simulations
Authors: Dong-Ho Lee, Hyundong Cho, Jonathan May, Jay Pujara
Date: 2025-02-24 00:00:00
Tasks: Question Generation, Question-Generation

Asking questions is a fundamental aspect of learning that facilitates deeper understanding. However, characterizing and crafting questions that effectively improve learning remains elusive. To address this gap, we propose QUEST (Question Utility Estimation with Simulated Tests). QUEST simulates a learning environment that enables the quantification of a question's utility based on its direct impact on improving learning outcomes. Furthermore, we can identify high-utility questions and use them to fine-tune question generation models with rejection sampling. We find that questions generated by models trained with rejection sampling based on question utility result in exam scores that are higher by at least 20% than those from specialized prompting grounded on educational objectives literature and models fine-tuned with indirect measures of question quality, such as saliency and expected information gain.
Mitigating Bias in RAG: Controlling the Embedder
Authors: Taeyoun Kim, Jacob Springer, aditi raghunathan, Maarten Sap
Date: 2025-02-24 00:00:00
Tasks: Fairness, RAG, Retrieval-augmented Generation

In retrieval augmented generation (RAG) systems, each individual component -- the LLM, embedder, and corpus -- could introduce biases in the form of skews towards outputting certain perspectives or identities. In this work, we study the conflict between biases of each component and their relationship to the overall bias of the RAG system, which we call bias conflict. Examining both gender and political biases as case studies, we show that bias conflict can be characterized through a linear relationship among components despite its complexity in 6 different LLMs. Through comprehensive fine-tuning experiments creating 120 differently biased embedders, we demonstrate how to control bias while maintaining utility and reveal the importance of reverse-biasing the embedder to mitigate bias in the overall system. Additionally, we find that LLMs and tasks exhibit varying sensitivities to the embedder bias, a crucial factor to consider for debiasing. Our results underscore that a fair RAG system can be better achieved by carefully controlling the bias of the embedder rather than increasing its fairness.
Linguistic Generalizability of Test-Time Scaling in Mathematical Reasoning
Authors: Guijin Son, Jiwoo Hong, Hyunwoo Ko, James Thorne
Date: 2025-02-24 00:00:00
Tasks: Math, Mathematical Reasoning

Scaling pre-training compute has proven effective for achieving mulitlinguality, but does the same hold for test-time scaling? In this work, we introduce MCLM, a multilingual math benchmark featuring competition-level problems in 55 languages. We test three test-time scaling methods-Outcome Reward Modeling (ORM), Process Reward Modeling (ORM), and Budget Forcing (BF)-on both Qwen2.5-1.5B Math and MR1-1.5B, a multilingual LLM we trained for extended reasoning. Our experiments show that using Qwen2.5-1.5B Math with ORM achieves a score of 35.8 on MCLM, while BF on MR1-1.5B attains 35.2. Although "thinking LLMs" have recently garnered significant attention, we find that their performance is comparable to traditional scaling methods like best-of-N once constrained to similar levels of inference FLOPs. Moreover, while BF yields a 20-point improvement on English AIME, it provides only a 1.94-point average gain across other languages-a pattern consistent across the other test-time scaling methods we studied-higlighting that test-time scaling may not generalize as effectively to multilingual tasks. To foster further research, we release MCLM, MR1-1.5B, and evaluation results.
V-SQL: A View-based Two-stage Text-to-SQL Framework
Authors: Zeshun You, Jiebin Yao, Dong Cheng, Zhiwei Wen, Zhiliang Lu, Xianyi Shen
Date: 2024-12-17 00:00:00
Tasks: Text to SQL, Text-To-SQL

The text-to-SQL task aims to convert natural language into Structured Query Language (SQL) without bias. Recently, text-to-SQL methods based on large language models (LLMs) have garnered significant attention. The core of mainstream text-to-SQL frameworks is schema linking, which aligns user queries with relevant tables and columns in the database. Previous methods focused on schema linking while neglecting to enhance LLMs' understanding of database schema. The complex coupling relationships between tables in the database constrain the SQL generation capabilities of LLMs. To tackle this issue, this paper proposes a simple yet effective strategy called view-based schema. This strategy aids LLMs in understanding the database schema by decoupling tightly coupled tables into low-coupling views. We then introduce V-SQL, a view-based two-stage text-to-SQL framework. V-SQL involves the view-based schema strategy to enhance LLMs' understanding of database schema. Results on the authoritative datasets Bird indicate that V-SQL achieves competitive performance compared to existing state-of-the-art methods.
DeepRTL: Bridging Verilog Understanding and Generation with a Unified Representation Model
Authors: Yi Liu, Changran Xu, Yunhao Zhou, Zeju Li, Qiang Xu
Date: 2025-02-20 00:00:00
Tasks: Code Generation, Semantic Similarity, Semantic Textual Similarity

Recent advancements in large language models (LLMs) have shown significant potential for automating hardware description language (HDL) code generation from high-level natural language instructions. While fine-tuning has improved LLMs' performance in hardware design tasks, prior efforts have largely focused on Verilog generation, overlooking the equally critical task of Verilog understanding. Furthermore, existing models suffer from weak alignment between natural language descriptions and Verilog code, hindering the generation of high-quality, synthesizable designs. To address these issues, we present DeepRTL, a unified representation model that excels in both Verilog understanding and generation. Based on CodeT5+, DeepRTL is fine-tuned on a comprehensive dataset that aligns Verilog code with rich, multi-level natural language descriptions. We also introduce the first benchmark for Verilog understanding and take the initiative to apply embedding similarity and GPT Score to evaluate the models' understanding capabilities. These metrics capture semantic similarity more accurately than traditional methods like BLEU and ROUGE, which are limited to surface-level n-gram overlaps. By adapting curriculum learning to train DeepRTL, we enable it to significantly outperform GPT-4 in Verilog understanding tasks, while achieving performance on par with OpenAI's o1-preview model in Verilog generation tasks.
LLMs in Mobile Apps: Practices, Challenges, and Opportunities
Authors: Kimberly Hau, Safwat Hassan, Shurui Zhou
Date: 2025-02-21 00:00:00

The integration of AI techniques has become increasingly popular in software development, enhancing performance, usability, and the availability of intelligent features. With the rise of large language models (LLMs) and generative AI, developers now have access to a wealth of high-quality open-source models and APIs from closed-source providers, enabling easier experimentation and integration of LLMs into various systems. This has also opened new possibilities in mobile application (app) development, allowing for more personalized and intelligent apps. However, integrating LLM into mobile apps might present unique challenges for developers, particularly regarding mobile device constraints, API management, and code infrastructure. In this project, we constructed a comprehensive dataset of 149 LLM-enabled Android apps and conducted an exploratory analysis to understand how LLMs are deployed and used within mobile apps. This analysis highlights key characteristics of the dataset, prevalent integration strategies, and common challenges developers face. Our findings provide valuable insights for future research and tooling development aimed at enhancing LLM-enabled mobile apps.