Algorithms of inference in a computer system oriented to input and semantic processing of text information are presented. Such inference is necessary for logical questions when the direct comparison of objects from a question and database can not give a result. The following classes of problems are considered: a check of hypotheses for persons and non-typical actions, the determination of persons and circumstances for non-typical actions, planning actions, the determination of event cause and state of persons. To form an answer both deduction and plausible reasoning are used. As a knowledge domain under consideration is social behavior of persons, plausible reasoning is based on laws of social psychology. Proposed algorithms of inference and plausible reasoning can be realized in computer systems closely connected with text processing (criminology, operation of business, medicine, document systems).
We study the interpretability issue of task-oriented dialogue systems in this paper. Previously, most neural-based task-oriented dialogue systems employ an implicit reasoning strategy that makes the model predictions uninterpretable to humans. To obtain a transparent reasoning process, we introduce neuro-symbolic to perform explicit reasoning that justifies model decisions by reasoning chains. Since deriving reasoning chains requires multi-hop reasoning for task-oriented dialogues, existing neuro-symbolic approaches would induce error propagation due to the one-phase design. To overcome this, …
Seemingly Plausible Distractors in Multi-Hop Reasoning: Are Large Language Models Attentive Readers?
ASUS Intelligent Cloud Services (AICS)（华硕智能云服务（AICS））；National University of Singapore（新加坡国立大学）；University of Manchester（曼彻斯特大学）；Imperial Global Singapore（帝国理工学院新加坡分部）
EVIT: Event-Oriented Instruction Tuning for Event Reasoning
School of Computer Science, Peking University（北京大学计算机学院）；Key Laboratory of High Confidence Software Technologies (PKU), MOE, China（高可信软件技术重点实验室（北京大学））；Advanced Institute of Big Data（大数据先进研究院）
Reasoning-Oriented and Analogy-Based Methods for Locating and Editing in Zero-Shot Event-Relational Reasoning
School of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）；School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；School of Computer Science and Artificial Intelligence, Liaoning Normal University（辽宁师范大学计算机与人工智能学院）
本文提出 ROLE 和 ABLE 两种定位与编辑方法，分别通过定位语言模型关键模块和利用任务间类比关系，高效提升零样本事件关系推理的可解释性与性能。
Learning from Failures: Correction-Oriented Policy Optimization with Verifiable Rewards
Chinese Information Processing Laboratory, Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所信息处理实验室）；University of Chinese Academy of Sciences（中国科学院大学）；Xiaohongshu Inc（小红书公司）
Microsoft Research Asia（微软亚洲研究院）；Westlake University（西湖大学）；Zhejiang University（浙江大学）
Non-task oriented dialogue systems have achieved great success in recent years due to largely accessible conversation data and the development of deep learning techniques. Given a context, current systems are able to yield a relevant and fluent response, but sometimes make logical mistakes because of weak reasoning capabilities. To facilitate the conversation reasoning research, we introduce MuTual, a novel dataset for Multi-Turn dialogue Reasoning, consisting of 8,860 manually annotated dialogues based on Chinese student English listening comprehension exams. Compared to previous benchmarks f…
OJBench: A Competition Level Code Benchmark For Large Language Models
Beijing University of Posts and Telecommunications（北京邮电大学）；Tsinghua University（清华大学）；University of Chinese Academy of Sciences（中国科学院大学）；Peking University（北京大学）；Moonshot AI
University of Southern California（南加州大学）；Information Sciences Institute, University of Southern California（南加州大学信息科学研究所）；University of California, San Diego（加州大学圣地亚哥分校）
Humans use natural language to compose common concepts from their environment into plausible, day-to-day scene descriptions. However, such generative commonsense reasoning (GCSR) skills are lacking in state-of-the-art text generation methods. Descriptive sentences about arbitrary concepts generated by neural text generation models (e.g., pre-trained text-to-text Transformers) are often grammatically fluent but may not correspond to human common sense, largely due to their lack of mechanisms to capture concept relations, to identify implicit concepts, and to perform generalizable reasoning abou…
Riazi-8B: An Urdu Large Language Model for Mathematical Reasoning
School of Electrical Engineering and Computer Science (SEECS)（电气工程与计算机科学学院）；National University of Sciences and Technology (NUST)（国家科学与技术大学）；Department of Communication, Quality Management and Information Systems（通信、质量管理与信息系统系）；Mid Sweden University（中瑞典大学）
Counterfactual Reasoning with Knowledge Graph Embeddings
Faculty of Computer Science, University of Vienna（维也纳大学计算机学院）；UniVie Doctoral School Computer Science, University of Vienna（维也纳大学UniVie计算机博士学院）；Faculty of Philological and Cultural Studies, University of Vienna（维也纳大学语文学与文化学院）
Allen Institute for AI（艾伦人工智能研究院）；Facebook AI（脸书人工智能研究院）；Paul G. Allen School of Computer Science & Engineering（保罗·G·艾伦计算机科学与工程学院）
Abductive reasoning is inference to the most plausible explanation. For example, if Jenny finds her house in a mess when she returns from work, and remembers that she left a window open, she can hypothesize that a thief broke into her house and caused the mess, as the most plausible explanation. While abduction has long been considered to be at the core of how people interpret and read between the lines in natural language (Hobbs et al., 1988), there has been relatively little research in support of abductive natural language inference and generation. We present the first study that investigat…
Mitigating Hallucination in Large Language Models: A Capability-Oriented Survey on RAG, Reasoning, and Agentic Systems
Electronic Information School, Wuhan University（武汉大学电子信息学院）；School of Computing, Dublin City University（都柏林城市大学计算机学院）；School of Public Health, Wuhan University（武汉大学公共卫生学院）；Insight Centre for Data Analytics, University of Galway（Galway大学数据分析研究中心）；Insight Centre for Data Analytics, Dublin City University（都柏林城市大学数据分析研究中心）
A Survey of Task-Oriented Knowledge Graph Reasoning: Status, Applications, and Prospects
School of Artificial Intelligence, Beihang University（北航人工智能学院）；School of Information Science and Technology, Beijing Forestry University（北京林业大学信息科学与技术学院）
Bridging Reasoning and Action: Hybrid LLM-RL Framework for Efficient Cross-Domain Task-Oriented Dialogue
School of Computer Science and Technology, Changsha University of Science and Technology（长沙理工大学计算机科学与技术学院）；School of Computer Science and Engineering, Central South University（中南大学计算机科学与工程学院）；Institute of Computing and Intelligence, Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳）计算与智能研究院）；University of Science and Technology Beijing（北京科技大学）；Text Computing and Cognitive Intelligence MOE Engineering Research Center, Guizhou University（贵州大学文字计算与智能认知MOE工程研究中心）
In recent years, large pretrained models have been used in dialogue systems to improve successful task completion rates. However, lack of reasoning capabilities of dialogue platforms make it difficult to provide relevant and fluent responses, unless the designers of a conversational experience spend a considerable amount of time implementing these capabilities in external rule based modules. In this work, we propose a novel method to fine-tune pretrained transformer models such as Roberta and T5. to reason over a set of facts in a given dialogue context. Our method includes a synthetic data ge…
Traditional systems designed for task oriented dialog utilize knowledge present only in structured knowledge sources to generate responses. However, relevant information required to generate responses may also reside in unstructured sources, such as documents. Recent state of the art models such as HyKnow and SeKnow aimed at overcoming these challenges make limiting assumptions about the knowledge sources. For instance, these systems assume that certain types of information, such as a phone number, is always present in a structured knowledge base (KB) while information about aspects such as en…
Thought-Path Contrastive Learning via Premise-Oriented Data Augmentation for Logical Reading Comprehension
School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications, Beijing Institute of Technology（北京理工大学海量语言信息处理与云计算应用北京工程研究中心）
Hebei University of Technology（河北工业大学）；West Virginia University（西弗吉尼亚大学）
The abductive natural language inference task ($α$NLI) is proposed to infer the most plausible explanation between the cause and the event. In the $α$NLI task, two observations are given, and the most plausible hypothesis is asked to pick out from the candidates. Existing methods model the relation between each candidate hypothesis separately and penalize the inference network uniformly. In this paper, we argue that it is unnecessary to distinguish the reasoning abilities among correct hypotheses; and similarly, all wrong hypotheses contribute the same when explaining the reasons of the observ…
Controlled natural languages (CNLs) are effective languages for knowledge representation and reasoning. They are designed based on certain natural languages with restricted lexicon and grammar. CNLs are unambiguous and simple as opposed to their base languages. They preserve the expressiveness and coherence of natural languages. In this report, we focus on a class of CNLs, called machine-oriented CNLs, which have well-defined semantics that can be deterministically translated into formal languages, such as Prolog, to do logical reasoning. Over the past 20 years, a number of machine-oriented CN…
In this paper, we investigate the tendency of end-to-end neural Machine Reading Comprehension (MRC) models to match shallow patterns rather than perform inference-oriented reasoning on RC benchmarks. We aim to test the ability of these systems to answer questions which focus on referential inference. We propose ParallelQA, a strategy to formulate such questions using parallel passages. We also demonstrate that existing neural models fail to generalize well to this setting.
Allen Institute for AI（艾伦人工智能研究所）；University of Washington（华盛顿大学）；University of California, Berkeley（加州大学伯克利分校）；Boston University（波士顿大学）；MIT-IBM Watson AI（麻省理工学院-IBM沃森人工智能实验室）
Humans have remarkable capacity to reason abductively and hypothesize about what lies beyond the literal content of an image. By identifying concrete visual clues scattered throughout a scene, we almost can't help but draw probable inferences beyond the literal scene based on our everyday experience and knowledge about the world. For example, if we see a "20 mph" sign alongside a road, we might assume the street sits in a residential area (rather than on a highway), even if no houses are pictured. Can machines perform similar visual reasoning? We present Sherlock, an annotated corpus of 103K i…
Ideological Bias in LLMs' Economic Causal Reasoning
Graduate School of Data Science, KAIST（韩国科学技术院数据科学研究生院）；College of Business, KAIST（韩国科学技术院商学院）；School of Computing, KAIST（韩国科学技术院计算机学院）；Division of Social Science, HKUST（香港科技大学社会科学系）
Abductive Reasoning is a task of inferring the most plausible hypothesis given a set of observations. In literature, the community has approached to solve this challenge by classifying/generating a likely hypothesis that does not contradict with a past observation and future observation. Some of the most well-known benchmarks that tackle this problem are aNLI and aNLG (pronounced as alpha-NLI and alpha-NLG). In this report, I review over some of the methodologies that were attempted to solve this challenge, re-implement the baseline models, and analyze some of the weaknesses that current appro…
CoSToM:Causal-oriented Steering for Intrinsic Theory-of-Mind Alignment in Large Language Models
National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, Huazhong University of Science and Technology（华中科技大学国家大数据技术与系统工程技术研究中心、服务计算技术与系统实验室、集群与网格计算实验室）；Singapore Management University（新加坡管理大学）
