GenAI4ELab/papercli-papers huggingface dataset:
PARA: Parameter-Efficient Fine-tuning with Prompt-Aware Representation Adjustment
Authors: Zequan Liu; Yi Zhao; Ming Tan; Wei Zhu; Aaron Xuxiang Tian
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: In the realm of parameter-efficient fine-tuning (PEFT) methods, while options like LoRA are available, there is a persistent demand in the industry for a PEFT approach that excels in both efficiency and performance within the context of single-backbone multi-tenant applications. This paper introduces a new and straightforward PEFT technique, termed Prompt Aware Representation Adjustment (PARA). The core of our proposal is to integrate a lightweight vector generator within each Transformer layer. This generator produces vectors that are responsive to input prompts, thereby adjusting the hidden representations accordingly. Our extensive experimentation across diverse tasks has yielded promising results. Firstly, the PARA method has been shown to surpass current PEFT benchmarks in terms of performance, despite having a similar number of adjustable parameters. Secondly, it has proven to be more efficient than LoRA in the single-backbone multi-tenant scenario, highlighting its significant potential for industrial adoption.
RAG4ITOps: A Supervised Fine-Tunable and Comprehensive RAG Framework for IT Operations and Maintenance
Authors: Tianyang Zhang; Zhuoxuan Jiang; Shengguang Bai; Tianrui Zhang; Lin Lin; Yang Liu; Jiawei Ren
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: With the ever-increasing demands on Question Answering (QA) systems for IT operations and maintenance, an efficient and supervised fine-tunable framework is necessary to ensure the data security, private deployment and continuous upgrading. Although Large Language Models (LLMs) have notably improved the open-domain QA’s performance, how to efficiently handle enterprise-exclusive corpora and build domain-specific QA systems are still less-studied for industrial applications. In this paper, we propose a general and comprehensive framework based on Retrieval Augmented Generation (RAG) and facilitate the whole business process of establishing QA systems for IT operations and maintenance. In accordance with the prevailing RAG method, our proposed framework, named with RAG4ITOps, composes of two major stages: (1) Models Fine-tuning & Data Vectorization, and (2) Online QA System Process. At the Stage 1, we leverage a contrastive learning method with two negative sampling strategies to fine-tune the embedding model, and design the instruction templates to fine-tune the LLM with a Retrieval Augmented Fine-Tuning method. At the Stage 2, an efficient process of QA system is built for serving. We collect enterprise-exclusive corpora from the domain of cloud computing, and the extensive experiments show that our method achieves superior results than counterparts on two kinds of QA tasks. Our experiment also provide a case for applying the RAG4ITOps to real-world enterprise-level applications.
ULMR: Unlearning Large Language Models via Negative Response and Model Parameter Average
Authors: Shaojie Shi; Xiaoyu Tan; Xihe Qiu; Chao Qu; Kexin Nie; Yuan Cheng; Wei Chu; Xu Yinghui; Yuan Qi
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: In recent years, large language models (LLMs) have attracted significant interest from the research community due to their broad applicability in many language-oriented tasks, and are now widely used in numerous areas of production and daily life. One source of the powerful capabilities of LLMs is the massive scale of their pre-training dataset. However, these pre-training datasets contain many outdated, harmful, and personally sensitive information, which inevitably becomes memorized by LLM during the pre-training process. Eliminating this undesirable data is crucial for ensuring the model’s safety and enhancing the user experience. However, the cost of extensively cleaning the pre-training dataset and retraining the model from scratch is very high. In this work, we propose ULMR , a unlearning framework for LLMs , which first uses carefully designed prompts to rewrite the instructions in the specified dataset, and generate corresponding negative responses. Subsequently, to ensure that the model does not excessively deviate post-training, we perform model parameter averaging to preserve the performance of the original LLM. We conducted experiments on two public datasets, TOFU and RWKU, demonstrating that our method can effectively forget specified information while retaining the capabilities of the original LLM.
Pretraining and Finetuning Language Models on Geospatial Networks for Accurate Address Matching
Authors: Saket Maheshwary; Arpan Paul; Saurabh Sohoney
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: We propose a novel framework for pretraining and fine-tuning language models with the goal of determining whether two addresses represent the same physical building. Address matching and building authoritative address catalogues are important to many applications and businesses, such as delivery services, online retail, emergency services, logistics, etc. We propose to view a collection of addresses as an address graph and curate inputs for language models by placing geospatially linked addresses in the same context. Our approach jointly integrates concepts from graph theory and weak supervision with address text and geospatial semantics. This integration enables us to generate informative and diverse address pairs, facilitating pretraining and fine-tuning in a self-supervised manner. Experiments and ablation studies on manually curated datasets and comparisons with state-of-the-art techniques demonstrate the efficacy of our approach. We achieve a 24.49% improvement in recall while maintaining 95% precision on average, in comparison to the current baseline across multiple geographies. Further, we deploy our proposed approach and show the positive impact of improving address matching on geocode learning.
SMARTCAL: An Approach to Self-Aware Tool-Use Evaluation and Calibration
Authors: Yuanhao Shen; Xiaodan Zhu; Lei Chen
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: The tool-use ability of Large Language Models (LLMs) has a profound impact on a wide range of applications. However, LLMs’ self-awareness and self-control capability in appropriately using tools remains understudied. The problem is consequential as it alarms a potential risk of degraded performance and poses a threat to trustworthiness on the models. In this paper, we conduct a study on a family of state-of-the-art LLMs on three datasets with two mainstream tool-use frameworks. Our study reveals the tool-abuse behavior of LLMs, a tendency for models to misuse tools along with models’ frequent overconfidence in tool choice. We also find that this is a common issue regardless of model capability. Accordingly, we propose a novel framework, SMARTCAL, to mitigate the observed issues, and our results show an average 8.6 percent increase in the QA performance in three testing datasets and 21.6 percent lower Expected Calibration Error (ECE) than existing methods.
Probing the Depths of Language Models’ Contact-Center Knowledge for Quality Assurance
Authors: Digvijay Anil Ingle; Aashraya Sachdeva; Surya Prakash Sahu; Mayank Sati; Cijo George; Jithendra Vepa
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Recent advancements in large Language Models (LMs) have significantly enhanced their capabilities across various domains, including natural language understanding and generation. In this paper, we investigate the application of LMs to the specialized task of contact-center Quality Assurance (QA), which involves evaluating conversations between human agents and customers. This task requires both sophisticated linguistic understanding and deep domain knowledge. We conduct a comprehensive assessment of eight LMs, revealing that larger models, such as Claude-3.5-Sonnet, exhibit superior performance in comprehending contact-center conversations. We introduce methodologies to transfer this domain-specific knowledge to smaller models by leveraging evaluation plans generated by more knowledgeable models, with optional human-in-the-loop refinement to enhance the capabilities of smaller models. Notably, our experimental results demonstrate an improvement of up to 18.95% in Macro F1 on an in-house QA dataset. Our findings emphasize the importance of evaluation plans in guiding reasoning and highlight the potential of AI-assisted tools to advance objective, consistent, and scalable agent evaluation processes in contact centers.
Structured Object Language Modeling (SO-LM): Native Structured Objects Generation Conforming to Complex Schemas with Self-Supervised Denoising
Authors: Amir Tavanaei; Kee Kiat Koo; Hayreddin Ceker; Shaobai Jiang; Qi Li; Julien Han; Karim Bouyarmane
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: In this paper, we study the problem of generating structured objects that conform to a complex schema, with intricate dependencies between the different components (facets) of the object. The facets of the object (attributes, fields, columns, properties) can be a mix of short, structured facts, or long natural-language descriptions. The object has to be self-consistent between the different facets in the redundant information it carries (relative consistency), while being grounded with respect to world knowledge (absolute consistency). We frame the problem as a Language Modeling problem (Structured Object Language Modeling) and train an LLM to perform the task natively, without requiring instructions or prompt-engineering. We propose a self-supervised denoising method to train the model from an existing dataset of such objects. The input query can be the existing object itself, in which case the system acts as a regenerator, completing, correcting, normalizing the input, or any unstructured blurb to be structured. We show that the self-supervised denoising training provides a strong baseline, and that additional supervised fine-tuning with small amount of human demonstrations leads to further improvement. Experimental results show that the proposed method matches or outperforms prompt-engineered general-purpose state-of-the-art LLMs (Claude 3, Mixtral-8x7B), while being order-of-magnitude more cost-efficient.
Assisting Breastfeeding and Maternity Experts in Responding to User Queries with an AI-in-the-loop Approach
Authors: Nadjet Bouayad-Agha; Ignasi Gomez-Sebastia; Alba Padro; Enric Pallares Roura; David Pelayo Castelló; Rocío Tovar
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Breastfeeding and Maternity experts are a scarce resource and engaging in a conversation with mothers on such a sensitive topic is a time-consuming effort. We present our journey and rationale in assisting experts to answer queries about Breastfeeding and Maternity topics from users, mainly mothers. We started by developing a RAG approach to response generation where the generated response is made available to the expert who has the option to draft an answer using the generated text or to answer from scratch. This was the start of an ongoing effort to develop a pipeline of AI/NLP-based functionalities to help experts understand user queries and craft their responses.
A Hassle-free Algorithm for Strong Differential Privacy in Federated Learning Systems
Authors: Hugh Brendan McMahan; Zheng Xu; Yanxiang Zhang
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Differential privacy (DP) and federated learning (FL) are combined as advanced privacy-preserving methods when training on-device language models in production mobile keyboard applications. DP-Follow-the-Regularized-Leader (DP-FTRL) algorithms, leveraging correlated noise mechanisms such as tree aggregation or matrix factorization, are widely used in practice for their superior privacy-utility trade-off and compatibility with FL systems. This paper presents a novel variant of DP-FTRL by adapting the recent theoretical advancements of the Buffered Linear Toeplitz (BLT) mechanism to multi-participant scenarios. In the FL setting, our BLT mechanism demonstrates enhanced privacy-utility trade-off and improved memory efficiency than the widely used tree aggregation mechanism. Moreover, BLT achieves comparable privacy and utility to the state-of-the-art banded matrix factorization mechanism, while significantly simplifying usage requirements and reducing memory. The flexibility of the BLT mechanism allows seamless integration with existing DP FL implementations in production environments. We evaluate the BLT-DP-FTRL algorithm on the StackOverflow dataset, serving as a research simulation benchmark, and across four on-device language model tasks in a production FL system. Our empirical results highlight the potential of the BLT mechanism to elevate the practicality and effectiveness of DP in real-world scenarios.
ProConSuL: Project Context for Code Summarization with LLMs
Authors: Vadim Lomshakov; Andrey Podivilov; Sergey Savin; Oleg Baryshnikov; Alena Lisevych; Sergey Nikolenko
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: We propose Project Context for Code Summarization with LLMs (ProConSuL), a new framework to provide a large language model (LLM) with precise information about the code structure from program analysis methods such as a compiler or IDE language services and use task decomposition derived from the code structure. ProConSuL builds a call graph to provide the context from callees and uses a two-phase training method (SFT + preference alignment) to train the model to use the project context. We also provide a new evaluation benchmark for C/C++ functions and a set of proxy metrics. Experimental results demonstrate that ProConSuL allows to significantly improve code summaries and reduce the number of hallucinations compared to the base model (CodeLlama-7B-instruct). We make our code and dataset available at https://github.com/TypingCat13/ProConSuL.
Retrieval Augmented Generation or Long-Context LLMs? A Comprehensive Study and Hybrid Approach
Authors: Zhuowan Li; Cheng Li; Mingyang Zhang; Qiaozhu Mei; Michael Bendersky
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Retrieval Augmented Generation (RAG) has been a powerful tool for Large Language Models (LLMs) to efficiently process overly lengthy contexts. However, recent LLMs like Gemini-1.5 and GPT-4 show exceptional capabilities to understand long contexts directly. We conduct a comprehensive comparison between RAG and long-context (LC) LLMs, aiming to leverage the strengths of both. We benchmark RAG and LC across various public datasets using three latest LLMs. Results reveal that when resourced sufficiently, LC consistently outperforms RAG in terms of average performance. However, RAG’s significantly lower cost remains a distinct advantage. Based on this observation, we propose Self-Route, a simple yet effective method that routes queries to RAG or LC based on model self-reflection. Self-Route significantly reduces the computation cost while maintaining a comparable performance to LC. Our findings provide a guideline for long-context applications of LLMs using RAG and LC.
A new approach for fine-tuning sentence transformers for intent classification and out-of-scope detection tasks
Authors: Tianyi Zhang; Atta Norouzian; Aanchan Mohan; Frederick Ducatelle
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: In virtual assistant (VA) systems it is important to reject or redirect user queries that fall outside the scope of the system. One of the most accurate approaches for out-of-scope (OOS) rejection is to combine it with the task of intent classification on in-scope queries, and to use methods based on the similarity of embeddings produced by transformer-based sentence encoders. Typically, such encoders are fine-tuned for the intent-classification task, using cross-entropy loss. Recent work has shown that while this produces suitable embeddings for the intent-classification task, it also tends to disperse in-scope embeddings over the full sentence embedding space. This causes the in-scope embeddings to potentially overlap with OOS embeddings, thereby making OOS rejection difficult. This is compounded when OOS data is unknown. To mitigate this issue our work proposes to regularize the cross-entropy loss with an in-scope embedding reconstruction loss learned using an auto-encoder. Our method achieves a 1-4% improvement in the area under the precision-recall curve for rejecting out-of-sample (OOS) instances, without compromising intent classification performance.
Tell me what I need to know: Exploring LLM-based (Personalized) Abstractive Multi-Source Meeting Summarization
Authors: Frederic Kirstein; Terry Ruas; Robert Kratel; Bela Gipp
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Meeting summarization is crucial in digital communication, but existing solutions struggle with salience identification to generate personalized, workable summaries, and context understanding to fully comprehend the meetings’ content.Previous attempts to address these issues by considering related supplementary resources (e.g., presentation slides) alongside transcripts are hindered by models’ limited context sizes and handling the additional complexities of the multi-source tasks, such as identifying relevant information in additional files and seamlessly aligning it with the meeting content.This work explores multi-source meeting summarization considering supplementary materials through a three-stage large language model approach: identifying transcript passages needing additional context, inferring relevant details from supplementary materials and inserting them into the transcript, and generating a summary from this enriched transcript.Our multi-source approach enhances model understanding, increasing summary relevance by ~9% and producing more content-rich outputs.We introduce a personalization protocol that extracts participant characteristics and tailors summaries accordingly, improving informativeness by ~10%.This work further provides insights on performance-cost trade-offs across four leading model families, including edge-device capable options.Our approach can be extended to similar complex generative tasks benefitting from additional resources and personalization, such as dialogue systems and action planning.
Detecting LLM-Assisted Cheating on Open-Ended Writing Tasks on Language Proficiency Tests
Authors: Chenhao Niu; Kevin P. Yancey; Ruidong Liu; Mirza Basim Baig; André Kenji Horie; James Sharpnack
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: The high capability of recent Large Language Models (LLMs) has led to concerns about possible misuse as cheating assistants in open-ended writing tasks in assessments. Although various detecting methods have been proposed, most of them have not been evaluated on or optimized for real-world samples from LLM-assisted cheating, where the generated text is often copy-typed imperfectly by the test-taker. In this paper, we present a framework for training LLM-generated text detectors that can effectively detect LLM-generated samples after being copy-typed. We enhance the existing transformer-based classifier training process with contrastive learning on constructed pairwise data and self-training on unlabeled data, and evaluate the improvements on a real-world dataset from the Duolingo English Test (DET), a high-stakes online English proficiency test. Our experiments demonstrate that the improved model outperforms the original transformer-based classifier and other baselines.
Can Machine Unlearning Reduce Social Bias in Language Models?
Authors: Omkar Dige; Diljot Arneja; Tsz Fung Yau; Qixuan Zhang; Mohammad Bolandraftar; Xiaodan Zhu; Faiza Khan Khattak
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Mitigating bias in language models (LMs) has become a critical problem due to the widespread deployment of LMs in the industry and customer-facing applications. Numerous approaches revolve around data pre-processing and subsequent fine-tuning of language models, tasks that can be both time-consuming and computationally demanding. As alternatives, machine unlearning techniques are being explored, yet there is a notable lack of comparative studies evaluating the effectiveness of these methods. In this work, we explore the effectiveness of two machine unlearning methods: Partitioned Contrastive Gradient Unlearning (PCGU) applied on decoder models, and Negation via Task Vector, and compare them with Direct Preference Optimization (DPO) to reduce social biases in open-source LMs such as LLaMA-2 and OPT. We also implement distributed PCGU for large models. It is empirically shown, through quantitative and qualitative analyses, that negation via Task Vector method outperforms PCGU and is comparable to DPO in debiasing models with minimum deterioration in model performance and perplexity. Negation via Task Vector reduces the bias score by 25.5% for LLaMA-2 and achieves bias reduction of up to 40% for OPT models. Moreover, it can be easily tuned to balance the trade-off between bias reduction and generation quality, unlike DPO.
Don’t be my Doctor! Recognizing Healthcare Advice in Large Language Models
Authors: Kellen Tan Cheng; Anna Lisa Gentile; Pengyuan Li; Chad DeLuca; Guang-Jie Ren
Venue: EMNLP 2024 (Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track)

Abstract: Large language models (LLMs) have seen increasing popularity in daily use, with their widespread adoption by many corporations as virtual assistants, chatbots, predictors, and many more. Their growing influence raises the need for safeguards and guardrails to ensure that the outputs from LLMs do not mislead or harm users. This is especially true for highly regulated domains such as healthcare, where misleading advice may influence users to unknowingly commit malpractice. Despite this vulnerability, the majority of guardrail benchmarking datasets do not focus enough on medical advice specifically. In this paper, we present the HeAL benchmark (HEalth Advice in LLMs), a health-advice benchmark dataset that has been manually curated and annotated to evaluate LLMs’ capability in recognizing health-advice - which we use to safeguard LLMs deployed in industrial settings. We use HeAL to assess several models and report a detailed analysis of the findings.