Large Language Models (LLMs) have emerged as powerful assistants for scientific writing.However, concerns remain about the quality and reliability of the generated text, including citation accuracy and faithfulness.While most recent work relies on methods such as LLMas-a-Judge, the reliability of LLM-as-a-Judge alone is also in doubt.In this work, we reframe citation evaluation as a problem of citation attribution alignment, which assesses whether LLM-generated citations match those a human author would include for the same text.We propose CiteGuard, a retrieval-aware agent framework designed to provide more faithful grounding for citation validation.CiteGuard improves over the prior baseline by 10 percentage points and achieves up to 68.1% accuracy on the CiteME benchmark, approaching human performance (69.2%).It also identifies alternative valid citations and demonstrates generalization ability for cross-domain citation attribution. 1

The hallucination of non-existent facts by LLMs is an important problem given its widespread adoption across various applications.Previous research addresses this problem by analyzing the internal parameterized knowledge boundaries to estimate confidence.However, these studies focus on the single-problem setting and have not explored the more challenging multi-problem setting, which requires accurately answering multiple questions simultaneously.We introduce a novel method for the multi-problem setting, Multiple Answers and Confidence Stepwise Tuning (MAC-Tuning), that separates the learning of answer prediction and confidence estimation during fine-tuning on instruction data.Extensive experiments demonstrate that our method outperforms baselines by up to 25% in average precision.

Identifying effective interventions from the scientific literature is challenging due to the high volume of publications, specialized terminology, and inconsistent reporting formats, making manual curation laborious and prone to oversight. To address this challenge, this paper proposes a novel framework leveraging large language models (LLMs), which integrates a progressive ontology prompting (POP) algorithm with a dual-agent system, named LLM-Duo. On the one hand, the POP algorithm conducts a prioritized breadth-first search (BFS) across a predefined ontology, generating structured prompt templates and action sequences to guide the automatic annotation process. On the other hand, the LLM-Duo system features two specialized LLM agents, an explorer and an evaluator, working collaboratively and adversarially to continuously refine annotation quality. We showcase the real-world applicability of our framework through a case study focused on speech-language intervention discovery. Experimental results show that our approach surpasses advanced baselines, achieving more accurate and comprehensive annotations through a fully automated process. Our approach successfully identified 2,421 interventions from a corpus of 64,177 research articles in the speech-language pathology domain, culminating in the creation of a publicly accessible intervention knowledge base with great potential to benefit the speech-language pathology community.

We explore and enhance the ability of neural language models to generate novel scientific directions grounded in literature.Work on literature-based hypothesis generation has traditionally focused on binary link predictionseverely limiting the expressivity of hypotheses.This line of work also does not focus on optimizing novelty.We take a dramatic departure with a novel setting in which models use as input background contexts (e.g., problems, experimental settings, goals), and output natural language ideas grounded in literature.We present SCIMON, a modeling framework that uses retrieval of "inspirations" from past scientific papers, and explicitly optimizes for novelty by iteratively comparing to prior papers and updating idea suggestions until sufficient novelty is achieved.Comprehensive evaluations reveal that GPT-4 tends to generate ideas with overall low technical depth and novelty, while our methods partially mitigate this issue.Our work represents a first step toward evaluating and developing language models that generate new ideas derived from the scientific literature 1 .

Identifying gene targets for enhancing metabolite production in metabolic engineering is challenging due to the vast research literature and the approximation in genome-scale metabolic model (GEM) simulations. To address this, we propose the Gene-Metabolite Association Prediction task, which automates gene discovery for given metabolite-gene pairs, accompanied by a benchmark dataset of 2474 metabolites and 1947 genes for Saccharomyces cerevisiae (SC) and Issatchenkia orientalis (IO). This task is complicated by incomplete metabolic graphs and metabolic heterogeneity. We introduce an Interactive Knowledge Transfer mechanism based on Metabolism Graphs (IKT4Meta) to enhance prediction accuracy by integrating cross-metabolism knowledge. Using Pretrained Language Models (PLMs) to generate inter-graph links mitigates heterogeneity issues, while intra-graph links are propagated via these anchors. Gene-metabolite predictions are then performed on the enriched graphs integrating multiple microorganisms’ knowledge. Experiments show that IKT4Meta outperforms baselines by up to 12.3% in link prediction.

Hongyi Liu, Qingyun Wang, Payam Karisani, Heng Ji. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

To assist human review process, we build a novel ReviewRobot to automatically assign a review score and write comments for multiple categories such as novelty and meaningful comparison.A good review needs to be knowledgeable, namely that the comments should be constructive and informative to help improve the paper; and explainable by providing detailed evidence.ReviewRobot achieves these goals via three steps: (1) We perform domainspecific Information Extraction to construct a knowledge graph (KG) from the target paper under review, a related work KG from the papers cited by the target paper, and a background KG from a large collection of previous papers in the domain.(2) By comparing these three KGs, we predict a review score and detailed structured knowledge as evidence for each review category.(3) We carefully select and generalize human review sentences into templates, and apply these templates to transform the review scores and evidence into natural language comments.Experimental results show that our review score predictor reaches 71.4%-100% accuracy.Human assessment by domain experts shows that 41.7%-70.5% of the comments generated by ReviewRobot are valid and constructive, and better than humanwritten ones for 20% of the time.Thus, Re-viewRobot can serve as an assistant for paper reviewers, program chairs and authors. 1

We present a PaperRobot who performs as an automatic research assistant by (1) conducting deep understanding of a large collection of human-written papers in a target domain and constructing comprehensive background knowledge graphs (KGs); (2) creating new ideas by predicting links from the background KGs, by combining graph attention and contextual text attention; (3) incrementally writing some key elements of a new paper based on memory-attention networks: from the input title along with predicted related entities to generate a paper abstract, from the abstract to generate conclusion and future work, and finally from future work to generate a title for a follow-on paper.Turing Tests, where a biomedical domain expert is asked to compare a system output and a human-authored string, show PaperRobot generated abstracts, conclusion and future work sections, and new titles are chosen over human-written ones up to 30%, 24% and 12% of the time, respectively. 1 keeps almost the same across years.In 2012, US scientists estimated that they read, on average, only 264 papers per year (1 out of 5000 available papers), which is, statistically, not different from what they reported in an identical survey last conducted in 2005.PaperRobot automatically reads existing papers to build background knowledge graphs (KGs), in which nodes are entities/concepts and edges are the relations between these entities (Section 2.2).

We aim to automatically generate natural language descriptions about an input structured knowledge base (KB).We build our generation framework based on a pointer network which can copy facts from the input KB, and add two attention mechanisms: (i) slot-aware attention to capture the association between a slot type and its corresponding slot value; and (ii) a new table position self-attention to capture the inter-dependencies among related slots.For evaluation, besides standard metrics including BLEU, METEOR, and ROUGE, we propose a KB reconstruction based metric by extracting a KB from the generation output and comparing it with the input KB.We also create a new data set which includes 106,216 pairs of structured KBs and their corresponding natural language descriptions for two distinct entity types.Experiments show that our approach significantly outperforms stateof-the-art methods.The reconstructed KB achieves 68.8% -72.6% F-score. 1