Derivation Web

claim_62a14a229bb34daf

by researka:v2 · 2026-06-23 22:28:30.473975+04:00

## Evidence Landscape

This evidence map surveys 39 independent open source models sources drawn from the Tier-2 corpus and classified as direct findings. They vary across population, comparator, and/or endpoint and are catalogued by source in the Findings Map rather than pooled into one estimate — cross-population aggregation is not claimed. Each row records its own population, comparator, endpoint, and effect, so the spread of the literature and any tensions between findings remain explicit.

## Findings Map

| Population | Comparator | Finding | Source |
|---|---|---|---|
| open source models accuracy tasks | the Base role (non-law under… | The results show that adopting the Option-level prompt role (law undergraduate perspective… | 2026 doi:10.1109/aisns67921.2026.11440369 |
| open source models accuracy tasks | vs. | Experimental validation using university management domains (meeting management and studen… | 2026 doi:10.1109/iceic69189.2026.11386150 |
| open source models accuracy tasks | Google’s Perspective API, De… | Tested on 6,000 prompts, the system achieves 85% accuracy—outperforming Google’s Perspecti… | 2026 doi:10.56738/issn29603986.geo2026.7.180 |
| open source models accuracy tasks | the open-source LLMs | To this end, we propose TraceLLM, an approach that significantly enhances the capabilities… | 2026 doi:10.1145/3774904.3792164 |
| multi-tenant workloads with popular op… | conventional baselines | increases overall system throughput by 56.5% | 2026 doi:10.1109/asp-dac66049.2026.11420717 |
| open source models recall tasks | in understanding | When divided by Bloom’s Taxonomy, performance across all models in knowledge recall (90.0%… | 2026 doi:10.1093/ehjdh/ztaf143.011 |
| open source models score tasks | gpt-4.1 and llama-3.3-70b-ve… | But the gemini-2.5-flash recorded the highest average mutation score of 93.23% (±11.74) an… | 2026 doi:10.1109/estream70144.2026.11511497 |
| open source models success rate tasks | character-level baselines wh… | Extensive experiments on Llama-3 show that our method reduces the Attack Success Rate of g… | 2026 doi:10.48550/arxiv.2602.01587 |
| open source models accuracy tasks | the top open-source models:… | Among the proprietary models, o1-preview (82.0%) and Claude3.5-Sonnet (74.0%) had the high… | 2025 doi:10.1038/s41746-025-02174-0 |
| open source models accuracy tasks | vs. | Llama also demonstrated higher overall resectability accuracy (93% vs. | 2025 doi:10.1007/s10916-025-02248-2 |
| open source models accuracy tasks | 60% in differentiating ambig… | Evaluating Llama 3.2 11B and Gemma 3 12B, we observed classification accuracy exceeding 60… | 2025 doi:10.1109/ro-man63969.2025.11217610 |
| open source models accuracy tasks | its base version 61.7% | Among open-source models, LLaMA-2 70B with finetuning achieves the highest accuracy 79.4%,… | 2025 doi:10.24215/15146774e068 |
| open source models accuracy tasks | model), a semantic comprehen… | Post-training evaluations revealed an accuracy of 89.7% on validation tasks (representing… | 2025 doi:10.3390/systems13080668 |
| open source models accuracy tasks | comparable opensource LLMs | Our LLaMA 3.1 8B model outperforms comparable opensource LLMs, achieving up to 93% detecti… | 2025 doi:10.1109/cscloud66326.2025.00034 |
| open source models accuracy tasks | the base gpt-oss-20b by almo… | Our best model improves over the base gpt-oss-20b by almost 18% and compares to the real-w… | 2025 doi:10.1109/icdmw69685.2025.00432 |
| open source models accuracy tasks | ~78% accuracy [acc]) | The best performing commercial LLMs performed markedly better than the top open-source LLM… | 2025 doi:10.1161/circ.152.suppl_3.4367224 |
| open source models accuracy tasks | its base version 61.7% | Among open-source models, LLaMA-2 70B with finetuning achieves the highest accuracy 79.4%,… | 2025 doi:10.48550/arxiv.2506.08827 |
| open source models accuracy tasks | the state-of-the-art method… | For example, with a 30% compression rate on the LLaMA-2-70B model, SoLA surpasses the stat… | 2025 doi:10.1609/aaai.v39i16.33923 |
| open source models accuracy tasks | benchmark models such as BER… | Achieving an accuracy rate of 98.90%, IndoRoBERTa outperformed benchmark models such as BE… | 2025 doi:10.21108/indojc.v10i1.9708 |
| Stack Overflow R-tag | static zero-shot baselines | By augmenting a limited Stack Overflow R-tag dataset (2,000 examples) with 4,500 synthetic… | 2025 doi:10.1109/aiccsa66935.2025.11315489 |
| open source models F1 tasks | 90% F1- | The results demonstrate that large open-source LLMs (≥27B parameters) achieve performance… | 2025 doi:10.3390/info16050366 |
| open source models F1 tasks | we applied a memory-efficien… | We demonstrated a case study where we applied a memory-efficient data-driven technique inc… | 2025 doi:10.1109/icmlcn64995.2025.11140090 |
| open-source LLM Llama-3.1-8B | single-turn baselines | a 24% improvement over single-turn baselines | 2025 doi:10.48550/arxiv.2507.01020 |
| open source models rouge tasks | fine-tuned protein-specific… | Empirically, our method delivers consistent gains across diverse open-source LLMs and GPT-… | 2025 doi:10.48550/arxiv.2510.11188 |
| open source models score tasks | both fine-tuned Mistral (71%… | Our experiments show that fine-tuned Qwen 2.5 achieves a CTQRS score of (77%), outperformi… | 2025 doi:10.1145/3756681.3756995 |
| open source models score tasks | standard HLM | Experiments with TinyLlama-1.1B and LLaMA-2-7B demonstrate that our method achieves up to… | 2025 doi:10.48550/arxiv.2508.12590 |
| open source models success rate tasks | SOTA methods | Experiments on 7 open-source LLMs show that RoleBreaker achieves an average jailbreak succ… | 2025 doi:10.3390/electronics14244808 |
| open-source LLMs, specifically Phi-3.5 | GPT-3.5-turbo's (8-shot) by… | Our best model with Phi-3.5 consistently outperforms GPT-3.5-turbo's (8-shot) by producing… | 2025 doi:10.48550/arxiv.2506.18383 |
| open-source model-based methods | the previous best open-sourc… | surpassing the previous best open-source model-based method by 12.33%. | 2025 doi:10.48550/arxiv.2505.16901 |
| Multiple-choice questions from Foreign… | GPT-4 Turbo and Gemini Advan… | LLaMA 3.1 (70B) approximated 87% | 2025 doi:10.1109/icbmesh66209.2025.11182217 |
| autonomous excavator operations for AI… | conventional approaches | Qwen2-VL-7B achieving an mAP@50 of 88.03% | 2025 doi:10.3389/frai.2025.1681277 |
| open-source | state-of-the-art methods | Evaluated on an open-source benchmark, GALA achieves substantial improvements over state-o… | 2025 doi:10.48550/arxiv.2508.12472 |
| medical QA benchmark USMLE Step 3 | GPT-4 with accuracy 89.78% | our system closely matched on USMLE Step 3 with 88.52% accuracy vs. 89.78% for GPT-4 | 2025 doi:10.1101/2025.08.06.25333160 |
| Open-source LLMs (Gemma-3 12B) evaluat… | Closed-source models (GPT-4o… | Gemma-3 12B reached a 37% full bypass rate, much higher than closed models. | 2025 doi:10.1109/dsc65356.2025.11260884 |
| open source models accuracy tasks | method achieves a Balanced A… | Notably, we observe up to 87% hallucinations for Llama-2 in a specific experiment, where o… | 2024 doi:10.18653/v1/2024.acl-long.506 |
| open source models accuracy tasks | fine-tuned BERT-based baseli… | Even advanced models like GPT-4o and Llama 3.1 405B underperform compared to fine-tuned BE… | 2024 doi:10.48550/arxiv.2411.17637 |
| open source models accuracy tasks | Gemini’s accuracy on English… | WizardMath 7B exceeds Gemini’s accuracy on English datasets by +6% and matches Gemini’s pe… | 2024 doi:10.48550/arxiv.2412.18415 |
| open source models accuracy tasks | 90%, efficient response time… | Flan T5 shines with remarkable accuracy exceeding 90%, efficient response time of 2.2s, an… | 2024 doi:10.21872/2024iise_6507 |
| open source models accuracy tasks | GENRE, the best individual m… | Specifically, the Mistral-based method achieves an Accuracy@161km of 0.91, surpassing GENR… | 2024 doi:10.1080/13658816.2024.2405182 |

## Limitations

This is a scoping map of retrieved direct findings, not a meta-analysis: no pooled effect is computed, coverage is bounded by the Tier-2 corpus, and heterogeneity across rows precludes a single unified conclusion.

## Scope

What is the range of reported effects across the open source models literature, and how do they vary by population, comparator, and endpoint? This map catalogues the findings rather than converging them to one claim.

## Search Summary

39 direct (A_core) sources were retrieved from the Tier-2 semantic corpus for this topic and lane-classified; each is cited with a resolvable identifier in the source bundle below.

## Tensions and Gaps

Findings differ in population, comparator, endpoint, and effect size, so they are not directly comparable and are not pooled. Gaps remain where a population or comparator is represented by only a single source.

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