1 day ago · Access the latest LLM leaderboard with comprehensive performance metrics and benchmark data. Compare top language models with interactive analysis tools.

We introduce an LLM framework for generating various NL datasets from Vega-Lite specifications. Our framework comprises of two novel prompting techniques to synthesize relevant chart semantics accurately and to enhance syntactic diversity, respectively.

Inside Chat2Plot, LLM does not generate Python code, but generates plot specifications in json. The declarative visualization specification in json is transformed into actual charts in Chat2Plot using plotly or altair, but users can also use json directly in their own applications.

Comparison and ranking the performance of over 30 AI models (LLMs) across key metrics including quality, price, performance and speed (output speed - tokens per second & latency - TTFT), context window & others. For more details including relating to …

We evaluate the chart-related ability of mainstream MLLMs and our ChartVLM on the proposed ChartX evaluation set. Extensive experiments demonstrate that ChartVLM surpasses both versatile and chart-related large models, achieving results comparable to GPT-4V.

ChartLlama outperforms all prior methods in ChartQA, Chart-to-text, and Chart-extraction evaluation benchmarks. Additionally, ChartLlama significantly improves upon the baseline in our specially compiled chart dataset, which includes new chart and task types.

open_llm_leaderboard. like 12.3k. Running on CPU Upgrade. App Files Files Community 1079 Fetching metadata from the HF Docker repository... Refreshing. Track, rank and evaluate open LLMs and chatbots Spaces. open-llm-leaderboard / open_llm_leaderboard. like 12.1k. Running on CPU Upgrade. App Files Files Community . 1058 ...

Feb 19, 2024 · In this paper, to comprehensively and rigorously benchmark the ability of the off-the-shelf MLLMs in the chart domain, we construct ChartX, a multi-modal evaluation set covering 18 chart types, 7 chart tasks, 22 disciplinary topics, and high-quality chart data.

We introduce VL2NL, a Large Language Model (LLM) framework that generates rich and diverse NL datasets using Vega-Lite specifications as input, thereby streamlining the development of Natural Language Interfaces (NLIs) for data visualization.

outperforms all prior methods in ChartQA, Chart-to-text, and Chart-extraction evaluation benchmarks. Additionally, ChartLlama significantly improves upon the baseline in our specially compiled chart dataset, which includes new chart and task types. The results of ChartLlama confirm the value 1 arXiv:2311.16483v1 [cs.CV] 27 Nov 2023