Reading and Summarization

• Adapter-based Selective Knowledge Distillation for Federated Multi-domain Meeting Summarization
  Xiachong Feng, Xiaocheng Feng, Xiyuan Du, Min-Yen Kan, Bing Qin [https://arxiv.org/abs/2308.03275](https://arxiv.org/abs/2308.03275) [[Routers]] [[Arxiv/Agents Multi|Agents Multi]] [[Reading Summari…
• Agent Laboratory: Using LLM Agents as Research Assistants
  This framework accepts a human-provided research idea and progresses through three stages—literature review, experimentation, and report writing to produce comprehensive research outputs, including a …
• Argument Summarization and its Evaluation in the Era of Large Language Models
  Large Language Models (LLMs) have revolutionized various Natural Language Generation (NLG) tasks, including Argument Summarization (ArgSum), a key subfield of Argument Mining (AM). This paper investig…
• Assessing the Ability of ChatGPT to Screen Articles for Systematic Reviews
  EUGENE SYRIANI, DIRO, Université de Montréal, Canada ISTVAN DAVID, DIRO, Université de Montréal, Canada GAURANSH KUMAR, DIRO, Université de Montréal, Canada “By organizing knowledge within a resear…
• CEO: Corpus-based Open-Domain Event Ontology Induction
  This paper presents CEO, a novel Corpus-based Event Ontology induction model to relax the restriction imposed by pre-defined event ontologies. Without direct supervision, CEO leverages distant supervi…
• Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models
  Retrieval-augmented language model (RALM) represents a significant advancement in mitigating factual hallucination by leveraging external knowledge sources. However, the reliability of the retrieved i…
• Context Embeddings for Efficient Answer Generation in RAG
  Retrieval-Augmented Generation (RAG) allows overcoming the limited knowledge of LLMs by extending the input with external information. As a consequence, the contextual inputs to the model become much …
• Don't "Overthink" Passage Reranking: Is Reasoning Truly Necessary?
  With the growing success of reasoning models across complex natural language tasks, researchers in the Information Retrieval (IR) community have begun exploring how similar reasoning capabilities can …
• Event-Aware Sentiment Factors from LLM-Augmented Financial Tweets: A Transparent Framework for Interpretable Quant Trading
  In this study, we wish to showcase the unique utility of large language models (LLMs) in financial semantic annotation and alpha signal discovery. Leveraging a corpus of company-related tweets, we use…
• Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences
  Decision conferences are structured, collaborative meetings that bring together experts from various fields to address complex issues and reach a consensus on recommendations for future actions or pol…
• From Articles to Code: On-Demand Generation of Core Algorithms from Scientific Publications
  ABSTRACT Maintaining software packages imposes significant costs due to dependency management, bug fixes, and versioning. We show that rich method descriptions in scientific publications can serve as…
• From Key Points to Key Point Hierarchy: Structured and Expressive Opinion Summarization
  KPA extracts the main points in the data as a list of concise sentences or phrases, termed key points, and quantifies their prevalence. While key points are more expressive than word clouds and key ph…
• From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting
  “Selecting the “right” amount of information to include in a summary is a difficult task. A good summary should be detailed and entity-centric without being overly dense and hard to follow. To better …
• Further Explorations on the Use of Large Language Models for Thematic Analysis. Open-Ended Prompts, Better Terminologies and Thematic Maps
  There is a nascent area, where scholars are approaching thematic analysis (TA) using LLMs, following the six phases developed by BRAUN and CLARKE (2006). TA is a qualitative method of analysis where t…
• Generating Query-Relevant Document Summaries via Reinforcement Learning
  E-commerce search engines often rely solely on product titles as input for ranking models with latency constraints. However, this approach can result in suboptimal relevance predictions, as product ti…
• Improving Document-Level Sentiment Analysis with User and Product Context
  “Document-level sentiment analysis aims to predict sentiment polarity of text that often takes the form of product or service reviews. Tang et al. (2015) demonstrated that modelling the individual who…
• LLMs as Architects and Critics for Multi-Source Opinion Summarization
  benchmark dataset for evaluating multi-source opinion summaries across 7 key dimensions: fluency, coherence, relevance, faithfulness, aspect coverage, sentiment consistency, specificity. Our results d…
• Language models are weak learners
  “A central notion in practical and theoretical machine learning is that of a weak learner, classifiers that achieve better-than-random performance (on any given distribution over data), even by a smal…
• Learn-by-interact: A Data-Centric Framework for Self-Adaptive Agents in Realistic Environments
  We construct task instructions using LLMs for each sub-trajectory, a process called backward construction. The synthesized data are then filtered and used for both training and in-context learning, wh…
• Neural Topic Modeling of Psychotherapy Sessions
  During the session, the dialogue between the patient and therapist are transcribed into pairs of turns. We take the full records of a patient, or a cohort of patients belonging to the same condition. …
• News Sentiment Embeddings for Stock Price Forecasting
  A key focus is to use news headlines from the Wall Street Journal (WSJ) to predict the movement of stock prices on a daily timescale with OpenAI-based text embedding models used to create vector encod…
• News Source Citing Patterns in AI Search Systems
  We address this gap by analyzing data from the AI Search Arena, a head-to-head evaluation platform for AI search systems. The dataset comprises over 24,000 conversations and 65,000 responses from mode…
• Proxona: Leveraging LLM-Driven Personas to Enhance Creators' Understanding of Their Audience
  we present Proxona, a system for defining and extracting representative audience personas from the comments. Creators converse with personas to gain insights into their preferences and engagement, sol…
• Reranking-based Generation for Unbiased Perspective Summarization
  Generating unbiased summaries in real-world settings such as political perspective summarization remains a crucial application of Large Language Models (LLMs). Yet, existing evaluation frameworks rely…
• SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval
  To address these issues, in this paper, we propose SAILER, a new Structure-Aware pre-traIned language model for LEgal case Retrieval. It is highlighted in the following three aspects: (1) SAILER fully…
• Self-critiquing models for assisting human evaluators
  We fine-tune large language models to write natural language critiques (natural language critical comments) using behavioral cloning. On a topic-based summarization task, critiques written by our mode…
• Self-reflective Uncertainties: Do LLMs Know Their Internal Answer Distribution?
  To reveal when a large language model (LLM) is uncertain about a response, uncertainty quantification commonly produces percentage numbers along with the output. But is this all we can do? We argue th…
• Summaries, Highlights, and Action items: Design, implementation and evaluation of an LLM-powered meeting recap system
  “Meetings play a critical infrastructural role in the coordination of work. In recent years, the nature of meetings have been changing with the shift to hybrid and remote work – meetings have moved in…
• Understanding Before Reasoning: Enhancing Chain-of-Thought with Iterative Summarization Pre-Prompting
  CoT encounters difficulties when key information required for the reasoning process is either implicit or missing. It primarily stems from the fact that CoT emphasizes the stages of reasoning, while n…
• Using Topic Models to Identify Clients’ Functioning Levels and Alliance Ruptures in Psychotherapy
  Computerized Natural Language Processing techniques can analyze psychotherapy sessions as texts; thus generating information about the therapy process and outcome and supporting the scaling-up of psyc…