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111 results for “LLMs”
Can LLMs Generate Higher Quality Code Than Humans? An Empirical Study
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LLMs as Teaching Assistants? Exploring New Horizons Through pTA - Dataset
<h1>LLMs as Teaching Assistants? Exploring New Horizons Through pTA - Dataset</h1> <p> </p> <h2>The Dataset</h2> <p> </p> <div>The dataset consists of logs of tests run by students through the pTA on their submissions. The collection, named <code>commandFinished</code> is a json file made up of a list of Mongodb objects. Each object is of the following format:</div> <div> </div> <div><code>{</code></div> <div><code> "_id": {</code></div> <div><code> "$oid": "6175e2d0e0375c2f95bd25c5"</code></div> <div><code> },</code></div> <div><code> "submissionGitRepoId": "gr0-ms1",</code></div> <div><code> "category": "git clone/pull",</code></div> <div><code> "finished": {</code></div> <div><code> "stdio": "",</code></div> <div><code> "stdError": "Cloning into '.'...\n",</code></div> <div><code> "wholeInteraction": "Running the process: docker run --network host --interactive --name ms1-gr0-echo-client ms1/gr0/echo-client.jar</code></div> <div><code> Testing if put works correctly... Client 1 puts\n-------Input@KV at localhost/127.0.0.1:33121> put dog one two three\n-------Output@KV at localhost/127.0.0.1:33121> put_success dog",</code></div> <div><code> "exitValue": 0</code></div> <div><code> },</code></div> <div><code> "finishedTime": {</code></div> <div><code> "$date": {</code></div> <div><code> "$numberLong": "1635122928975"</code></div> <div><code> }</code></div> <div><code> },</code></div> <div><code> "durationmillis": 884,</code></div> <div><code> "isFinal": false</code></div> <div><code>}</code></div> <p> </p> <div>where</div> <p> </p> <div>- <code>submissionGitRepoId</code> - An ID created of the format <code><gr>-<ms></code>, where <code><gr></code> is the group id and <code><ms></code> is the milestone number</div> <div>- <code>finished</code> - The object containing details of the test</div> <p> </p> <h2>How to import</h2> <p> </p> <div>- Install <a title="MongoDB and MongoDB Compass" href="https://www.mongodb.com/">MongoDB and MongoDB Compass</a> .</div> <div>- Run a MongoDB deployment.</div> <div>- Open MongoDB Compass and connect to the deployment. Do so by entering the <code>URI</code> and clicking on <code>Connect</code>.</div> <div>- Create a new database and navigate to it.</div> <div>- Create a new collection and navigate to it.</div> <div>- On the resulting page, click on the <code>ADD DATA</code> dropdown and select <code>Import JSON or CSV file</code>.</div> <div>- Confirm the <code>commandFinished.json</code> file that you would like to import.</div> <p> </p>
Artifact for "Are LLMs all we need in code comparison tasks?"
<p>This is the artifact for paper "Are LLMs all we need in code comparison tasks?"</p> <p>It includes all the datasets and results.</p>
【outputs_encode_to_representations】only two 8B LLMs, only FB task, only augmented responses language materials w/o their representations, no original prompt or response
<ul> <li>only two 8B LLMs,</li> <li>only FB task,</li> <li>only augmented responses language materials w/o their representations, no original prompt or response</li> <li>19 (valid prompts) ⨉ 2 (LLMs) = 38 (files)</li> </ul>
【outputs_encode_to_representations】only two 8B LLMs, only Hinting task, only augmented responses language materials w/o their representations, no original prompt or response
<ul> <li>only two 8B LLMs, only Hinting task,</li> <li>only augmented responses language materials w/o their representations,</li> <li>no original prompt or response,</li> <li>13 (valid prompts) ⨉ 2 (scores) = 26 files</li> </ul>
【outputs_encode_to_representations】only two 8B LLMs, only Ir task, only augmented responses language materials w/o their representations, no original prompt or response
<ul> <li>only two 8B LLMs,</li> <li>only Ir task, only augmented responses language materials w/o their representations,</li> <li>no original prompt or response,</li> <li>12 (valid prompts) ⨉ 2 (scores) = 24 files</li> </ul>
【outputs_encode_to_representations】only two 8B LLMs, only SS task, augmented responses representations w/o their language materials, w/ original prompt and response representations
<ul> <li>only two 8B LLMs,</li> <li>only SS task,</li> <li>augmented responses representations w/o their language materials,<br> <ul> <li>12 (valid prompts), 2 (LLMs), 2 (Scores)</li> <li>16 ⨉ 2 (LLMs) = 32 files</li> </ul> </li> <li>original prompt and response representations <ul> <li>[1 (prompt) + 1 (response)] ⨉ 2 (LLMs) = 4 files</li> </ul> </li> <li>in total, 32 + 4 = 36 files</li> </ul>
【outputs_encode_to_representations】only two 8B LLMs, only Ir task, augmented responses representations w/o their language materials, w/ original prompt and response representations
<ul> <li>only two 8B LLMs,</li> <li>only Ir task, augmented responses representations w/o their language materials,<br> <ul> <li>12 (valid prompts) ⨉ 2 (LLMs) ⨉ 2 (scores) = 48 files</li> </ul> </li> <li>original prompt and response representations <ul> <li>[1 (prompt) + 1 (response)] ⨉ 2 (LLMs) = 4 files</li> </ul> </li> <li>in total, 48 + 4 = 52 files</li> </ul>
【outputs_encode_to_representations】only two 8B LLMs, only Hinting task, augmented responses representations w/o their language materials, w/ original prompt and response representations
<ul> <li>only two 8B LLMs, only Hinting task,</li> <li>augmented responses representations w/o their language materials,<br> <ul> <li>13 (valid prompts) ⨉ 2 (LLMs) ⨉ 2 (scores) = 52 files</li> </ul> </li> <li>original prompt and response representations, <ul> <li>[1 (prompt) + 1 (response)] ⨉ 2 (LLMs) = 4 files</li> </ul> </li> <li>in total, 52 + 4 = 56 files</li> </ul>
【outputs_encode_to_representations】only two 8B LLMs, only SS 012 task, only augmented responses language materials w/o their representations, no original prompt or response
<ul> <li>only two 8B LLMs,</li> <li>only SS 012 task, only augmented responses language materials w/o their representations,</li> <li>no original prompt or response language materials</li> <li>7 (valid prompts) ⨉ 1 (Score) = 7 files</li> </ul>
【outputs_encode_to_representations】only two 8B LLMs, only SS task, only augmented responses language materials w/o their representations, no original prompt or response
<ul> <li>only two 8B LLMs,</li> <li>only SS task,</li> <li>only augmented responses language materials w/o their representations,</li> <li>no original prompt or response language materials</li> <li>12 (valid prompts), 2 (Scores)</li> <li>16 files</li> </ul>
【outputs_encode_to_representations】only two 8B LLMs, only FB task, augmented responses representations w/o their language materials, w/ original prompt and response representations
<ul> <li>only two 8B LLMs</li> <li>only FB task</li> <li>augmented responses representations w/o their language materials <ul> <li>19 (valid prompts) ⨉ 2 (LLMs) ⨉ 2 (scores) = 76 files </li> </ul> </li> <li>original prompt and response representations <ul> <li>[1 (prompt) + 1 (response)] ⨉ 2 (LLMs) = 4 files</li> </ul> </li> <li>in total, 76 + 4 = 80 files</li> </ul>
【outputs_encode_to_representations】only two 8B LLMs, only SS 012 task, augmented responses representations w/o their language materials, w/ original prompt and response representations
<ul> <li>only two 8B LLMs,</li> <li>only SS 012 task, only augmented responses representations w/o their language materials,<br> <ul> <li>7 (valid prompts) ⨉ 2 (LLMs) ⨉ 1 (Scores) = 14 files</li> </ul> </li> <li>original prompt and response representations <ul> <li>1 (response) ⨉ 2 (LLMs) = 2 files</li> </ul> </li> <li>in total, 14 + 2 = 16 files</li> </ul>
【outputs_encode_to_representations】only two 70B LLMs, only Hinting task, only augmented responses language materials w/o their representations, no original prompt or response
<ul> <li>only two 70B LLMs,</li> <li>only Hinting task,</li> <li>only augmented responses language materials w/o their representations,</li> <li>no original prompt or response,</li> <li>13 (valid prompts) ⨉ 2 (scores) = 26 files</li> </ul>
【outputs_encode_to_representations】only two 70B LLMs, only FB task, only augmented responses language materials w/o their representations, no original prompt or response
<ul> <li>only two 70B LLMs, </li> <li>only FB task,</li> <li>only augmented responses language materials w/o their representations,</li> <li>no original prompt or response,</li> <li>19 (valid prompts) ⨉ 2 (scores) = 38 files</li> </ul>
【outputs_encode_to_representations】only two 70B LLMs, only Ir task, only augmented responses language materials w/o their representations, no original prompt or response
<ul> <li>only two 70B LLMs,</li> <li>only Ir task,</li> <li>only augmented responses language materials w/o their representations,</li> <li>no original prompt or response,</li> <li>12 (valid prompts) ⨉ 2 (scores) = 24 files</li> </ul>
【outputs_encode_to_representations】only two 70B LLMs, only SS 012 task, only augmented responses language materials w/o their representations, no original prompt or response
<ul> <li>only two 70B LLMs,</li> <li>only SS 012 task,</li> <li>only augmented responses language materials w/o their representations,</li> <li>no original prompt or response language materials</li> <li>7 (valid prompts) ⨉ 1 (Score) = 7 files</li> </ul>
【outputs_encode_to_representations】only two 70B LLMs, only SS task, only augmented responses language materials w/o their representations, no original prompt or response
<ul> <li>only two 70B LLMs,</li> <li>only SS task,</li> <li>only augmented responses language materials w/o their representations,</li> <li>no original prompt or response language materials</li> <li>12 (valid prompts), 2 (Scores), 16 files</li> </ul>
【outputs_encode_to_representations】only two 70B LLMs, only Hinting task, augmented responses representations w/o their language materials, w/ original prompt and response representations
<ul> <li>only two 70B LLMs,</li> <li>only Hinting task, augmented responses representations w/o their language materials, <ul> <li>13 (valid prompts) ⨉ 2 (LLMs) ⨉ 2 (scores) = 52 files</li> </ul> </li> <li>original prompt and response representations, <ul> <li>[1 (prompt) + 1 (response)] ⨉ 2 (LLMs) = 4 files</li> </ul> </li> <li>in total, 52 + 4 = 56 files</li> </ul>
【outputs_encode_to_representations】only two 70B LLMs, only FB task, augmented responses representations w/o their language materials, w/ original prompt and response representations
<ul> <li>only two 70B LLMs</li> <li>only FB task augmented responses representations w/o their language materials <ul> <li>19 (valid prompts) ⨉ 2 (LLMs) ⨉ 2 (scores) = 76 files </li> </ul> </li> <li>original prompt and response representations <ul> <li>[1 (prompt) + 1 (response)] ⨉ 2 (LLMs) = 4 files</li> </ul> </li> <li>in total, 76 + 4 = 80 files</li> </ul>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.