The first GroundLM shared tasks evaluate whether language models can identify the evidence behind an answer, rather than merely produce a correct answer in isolation. GoldenViewVQA requires a model to answer a multiple-choice question about six synchronized driving-camera views and identify the supporting view. LitTraceQA requires systems to retrieve scientific papers, locate answer-bearing evidence, and return multiple-choice or structured-table answers. We summarize the task design, submitted systems, official results, and recurrent failure modes. Among teams that submitted system reports, the best official joint accuracy on GoldenViewVQA is 0.8814. Answer accuracy is consistently higher than view accuracy, while macro view accuracy remains lower than micro accuracy, exposing both attribution errors and view imbalance. On the required LitTraceQA test set, the best score among teams that submitted system reports is 0.8002. Paper retrieval approaches saturation for several systems, but evidence F1 and structured-table accuracy remain substantially lower. Across both tasks, the central finding is consistent: producing a correct answer is easier than recovering the exact evidence representation required to justify it. We also report an optional 4,901-question LitTraceQA diagnostic split, whose unverified labels make it useful for stress testing but not for ranking systems on the main task.

Scientific question answering is useful only when readers can recover the papers and evidence behind an answer. Yet many benchmarks either provide the source document or score only the final response, obscuring failures in literature retrieval and evidence localization. We present LitTraceQA, a literature-grounded question answering benchmark in which a system must return a connected trace: canonical paper identifiers, typed evidence locations, and a checkable answer. The collection contains 4,978 unique questions grounded in 4,859 papers and covers tables, figures, text spans, equations or algorithms, and citation contexts. Of these questions, 64.85% require multiple papers; answers use multiple-choice or structured-table formats; and 61.9% are missed by every memory-only challenger used during construction. We describe a generate-and-ground-challenge pipeline and an evaluation protocol that reports paper retrieval, evidence grounding, answer correctness, and strict joint success separately. LitTraceQA therefore tests whether scientific QA systems can produce answers whose provenance is inspectable, rather than merely plausible.

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 LLM-as-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.

How can large language models (LLMs) process and translate endangered languages? Many languages lack a large corpus to train a decent LLM; therefore existing LLMs rarely perform well in unseen, endangered languages. On the contrary, we observe that 2000 endangered languages, though without a large corpus, have a grammar book or a dictionary. We propose LingoLLM, a training-free approach to enable an LLM to process unseen languages that hardly occur in its pre-training. Our key insight is to demonstrate linguistic knowledge of an unseen language in an LLM’s prompt, including a dictionary, a grammar book, and morphologically analyzed input text. We implement LingoLLM on top of two models, GPT-4 and Mixtral, and evaluate their performance on 5 tasks across 8 endangered or low-resource languages. Our results show that LingoLLM elevates translation capability from GPT-4’s 0 to 10.5 BLEU for 10 language directions. Our findings demonstrate the tremendous value of linguistic knowledge in the age of LLMs for endangered languages. Our data, code, and model generations will be released to the public. Our data, code, and model generations can be found at https://github.com/LLiLab/llm4endangeredlang.