Ror-bench: Revealing Recitation Over Reasoning In Large Language Models Through Subtle Context Shifts

In caller years, nan accelerated advancement of LLMs has fixed nan belief that we are nearing nan accomplishment of Artificial General Intelligence (AGI), pinch models seemingly tin of solving progressively analyzable tasks. However, a basal mobility remains: Are LLMs genuinely reasoning for illustration humans aliases simply repeating patterns learned during training? Since nan merchandise of models for illustration GPT-3 and ChatGPT, LLMs person revolutionized nan investigation landscape, pushing boundaries crossed AI and science. Data quality, exemplary scaling, and multi-step reasoning improvements person brought LLMs adjacent to passing high-level AGI benchmarks. Yet, their existent reasoning capabilities are not afloat understood. Instances wherever precocious models neglect to lick elemental mathematics problems—despite their evident simplicity—raise concerns astir whether they are genuinely reasoning aliases conscionable mimicking acquainted solution patterns.

Although various benchmarks beryllium to measure LLMs crossed domains for illustration wide knowledge, coding, math, and reasoning, galore trust connected tasks solvable by applying memorized templates. As a result, nan existent intelligence and robustness of LLMs stay debatable. Studies show LLMs struggle pinch subtle discourse shifts, elemental calculations, symbolic reasoning, and out-of-distribution prompts. These weaknesses are amplified nether perturbed conditions aliases misleading cues. Similarly, multi-modal LLMs, including vision-language models for illustration GPT-4v and LLaVA, show nan aforesaid inclination to singing alternatively of logic erstwhile tested pinch subtly altered ocular aliases textual inputs. This suggests that issues for illustration spurious correlations, memorization, and inefficient decoding mightiness underlie these failures, indicating a spread betwixt observed capacity and genuine understanding.

ByteDance Seed and nan University of Illinois Urbana-Champaign researchers present RoR-Bench, a caller multi-modal benchmark designed to place whether LLMs trust connected recitation alternatively than genuine reasoning erstwhile solving elemental problems pinch subtly altered conditions. The benchmark includes 158 matter and 57 image problem pairs, each featuring a basal reasoning task alongside a somewhat modified version. Experiments uncover that starring models for illustration OpenAI-o1 and DeepSeek-R1 suffer drastic capacity drops—often complete 60% pinch insignificant changes. Alarmingly, astir models struggle to admit unsolvable problems—preliminary fixes for illustration punctual engineering connection constricted improvement, emphasizing nan request for deeper solutions.

RoR-Bench is simply a Chinese multimodal benchmark created to measure whether LLMs trust connected memorized solution patterns alternatively than existent reasoning. It contains 215 problem pairs—158 text-based and 57 image-based—where each brace includes an original and a subtly altered version. The original problems are simple, often from children’s puzzle sets, while nan modified ones present insignificant changes that require wholly different reasoning. Annotators ensured minimal wording changes and nary ambiguity. Notably, immoderate problems are designed to person nary solution aliases characteristic unrelated information, testing LLMs’ expertise to admit illogical conditions and defy recitation-based answers.

The study empirically evaluates starring LLMs and VLMs connected nan RoR-Bench benchmark, focusing connected their expertise to logic done subtle problem changes alternatively than simply recalling learned patterns. Results uncover that astir models suffer a important capacity drop—often complete 50% erstwhile tested connected somewhat modified problems, suggesting a reliance connected mahfuz alternatively than genuine reasoning. Even techniques for illustration Chain-of-Thought prompting aliases “Forced Correct” instructions supply constricted improvement. Few-shot in-context learning shows immoderate gains, particularly pinch accrued examples aliases added instructions, but still fails to adjacent nan gap. Overall, these findings item nan limitations of existent models successful adaptive reasoning.

In conclusion, nan study introduces RoR-Bench, a Chinese multimodal benchmark designed to uncover a captious flaw successful existent ample connection models: their inability to grip elemental reasoning tasks erstwhile problem conditions are somewhat altered. The important capacity drop—often complete 50% suggests that these models trust connected mahfuz alternatively than existent reasoning. Even pinch added prompts aliases few-shot examples, nan rumor remains mostly unresolved. While nan benchmark is constricted to Chinese, first English results bespeak akin weaknesses. The findings situation assumptions astir LLM intelligence and telephone for early investigation to create models that logic genuinely alternatively than reciting learned patterns from training data.

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